{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "\n",
    "# # Consider adding the following from Ben Smith's tutorial\n",
    "# import os\n",
    "# import h5py\n",
    "# from glob import glob\n",
    "# import numpy as np\n",
    "# import matplotlib.pyplot as plt\n",
    "# import scipy.signal\n",
    "## data_dir='ATL06/Byrd_glacier_rel001/'\n",
    "\n",
    "import os\n",
    "# import sys\n",
    "# module_path = os.path.abspath(os.path.join('..'))\n",
    "# if module_path not in sys.path:\n",
    "#     sys.path.append(module_path)\n",
    "\n",
    "\n",
    "# # make sure we're dealing with the most recent version of any code we're using\n",
    "# %load_ext autoreload\n",
    "# %autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/data/nsidc/161027313/processed_ATL06_20181019231110_03260102_001_01.h5\n",
      "/home/jovyan/data/nsidc/161076435/processed_ATL06_20181030110205_04860106_001_01.h5\n",
      "/home/jovyan/data/nsidc/161253216/processed_ATL06_20190118185056_03260202_001_01.h5\n"
     ]
    }
   ],
   "source": [
    "! ls ~/data/nsidc/**/*.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establish directories\n",
    "data_dir = '/home/jovyan/data/nsidc/**/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/jovyan/data/nsidc/161027313/processed_ATL06_20181019231110_03260102_001_01.h5',\n",
       " '/home/jovyan/data/nsidc/161076435/processed_ATL06_20181030110205_04860106_001_01.h5',\n",
       " '/home/jovyan/data/nsidc/161253216/processed_ATL06_20190118185056_03260202_001_01.h5']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fns = []\n",
    "\n",
    "# Get the filenames and append to a list\n",
    "for f in glob.glob(data_dir + \"*.h5\"):\n",
    "    # append full filename to list of filenames\n",
    "    fns.append(f)  \n",
    "\n",
    "# sort the list\n",
    "fns.sort()\n",
    "fns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jovyan/data/nsidc/161253216/processed_ATL06_20190118185056_03260202_001_01.h5'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify the last HDF5 file from the list as a test case\n",
    "myfile_fn = fns[-1]\n",
    "myfile_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inspect metadata with Fernando Paolo's python function\n",
    "\n",
    "def print_attrs(name, obj):\n",
    "    print(name)\n",
    "    for key,val in obj.attrs.items():\n",
    "        print(\"    %s: %s\" % (key, val))\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # This outputs a whole lot of metadata\n",
    "# f = h5py.File(myfile_fn, 'r')  # keep it open in read mode\n",
    "\n",
    "# # Inspect base groups quickly\n",
    "# print(f.keys())\n",
    "\n",
    "# # print attributes using previously-defined function\n",
    "# f.visititems(print_attrs)   \n",
    "\n",
    "# f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ATL06_to_dict(filename, dataset_dict):\n",
    "    \"\"\"\n",
    "        Read selected datasets from an ATL06 file\n",
    "\n",
    "        Input arguments:\n",
    "            filename: ATl06 file to read\n",
    "            dataset_dict: A dictinary describing the fields to be read\n",
    "                    keys give the group names to be read, \n",
    "                    entries are lists of datasets within the groups\n",
    "        Output argument:\n",
    "            D6: dictionary containing ATL06 data.  Each dataset in \n",
    "                dataset_dict has its own entry in D6.  Each dataset \n",
    "                in D6 contains a list of numpy arrays containing the \n",
    "                data\n",
    "    \"\"\"\n",
    "    \n",
    "    D6=[]\n",
    "    pairs=[1, 2, 3]\n",
    "    beams=['l','r']\n",
    "    # open the HDF5 file\n",
    "    with h5py.File(filename) as h5f:\n",
    "        # loop over beam pairs\n",
    "        for pair in pairs:\n",
    "            # loop over beams\n",
    "            for beam_ind, beam in enumerate(beams):\n",
    "                # check if a beam exists, if not, skip it\n",
    "                if '/gt%d%s/land_ice_segments' % (pair, beam) not in h5f:\n",
    "                    continue\n",
    "                # loop over the groups in the dataset dictionary\n",
    "                temp={}\n",
    "                for group in dataset_dict.keys():\n",
    "                    for dataset in dataset_dict[group]:\n",
    "                        DS='/gt%d%s/%s/%s' % (pair, beam, group, dataset)\n",
    "                        # since a dataset may not exist in a file, we're going to try to read it, and if it doesn't work, we'll move on to the next:\n",
    "                        try:\n",
    "                            temp[dataset]=np.array(h5f[DS])\n",
    "                            # some parameters have a _FillValue attribute.  If it exists, use it to identify bad values, and set them to np.NaN\n",
    "                            if '_FillValue' in h5f[DS].attrs:\n",
    "                                fill_value=h5f[DS].attrs['_FillValue']\n",
    "                                \n",
    "                                bad=temp[dataset]==fill_value\n",
    "                                temp[dataset]=np.float32(temp[dataset])\n",
    "                                temp[dataset][bad]=np.NaN\n",
    "#                                 temp[dataset][temp[dataset]==fill_value]=np.NaN\n",
    "                        except KeyError as e:\n",
    "                            pass\n",
    "                        except TypeError:\n",
    "                            print(type(fill_value))\n",
    "                if len(temp) > 0:\n",
    "                    # it's sometimes convenient to have the beam and the pair as part of the output data structure: This is how we put them there.\n",
    "                    temp['pair']=np.zeros_like(temp['h_li'])+pair\n",
    "                    temp['beam']=np.zeros_like(temp['h_li'])+beam_ind\n",
    "                    temp['filename']=filename\n",
    "                    D6.append(temp)\n",
    "    return D6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dict={'land_ice_segments':['h_li', 'delta_time','longitude','latitude'], 'land_ice_segments/ground_track':['x_atc']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'h_li': array([      nan, 1598.1129, 1599.9465], dtype=float32),\n",
       " 'delta_time': array([33072997.56126441, 33072997.60031595, 33072997.60595344]),\n",
       " 'longitude': array([-121.90938255, -121.9098725 , -121.90992424]),\n",
       " 'latitude': array([48.71004147, 48.71254447, 48.71290321]),\n",
       " 'pair': array([1., 1., 1.], dtype=float32),\n",
       " 'beam': array([0., 0., 0.], dtype=float32),\n",
       " 'filename': '/home/jovyan/data/nsidc/161253216/processed_ATL06_20190118185056_03260202_001_01.h5'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# read ATL06 into a dictionary (the ATL06 file has the same name as the ATL03 file, except for the product name)\n",
    "ATL06_file=myfile_fn\n",
    "D6_list=ATL06_to_dict(ATL06_file, dataset_dict)\n",
    "\n",
    "# pick out gt1r:\n",
    "D6 = D6_list[0]\n",
    "D6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot granules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 18 beam/pair combinations\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fbc24efa080>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.6/site-packages/matplotlib/colors.py:885: UserWarning: Warning: converting a masked element to nan.\n",
      "  dtype = np.min_scalar_type(value)\n",
      "/srv/conda/lib/python3.6/site-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n",
      "  data = np.array(a, copy=False, subok=subok)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib widget\n",
    "\n",
    "D6=[]\n",
    "pairs=[1, 2, 3]\n",
    "beams=['l','r']\n",
    "\n",
    "files=glob.glob(data_dir+'/*.h5')\n",
    "for file in files:\n",
    "    this_name=os.path.basename(file)\n",
    "    D6 += ATL06_to_dict(file, dataset_dict)\n",
    "print(\"read %d beam/pair combinations\" % (len(D6)))\n",
    "\n",
    "# now plot the results:\n",
    "plt.figure();\n",
    "for Di in D6:\n",
    "    plt.scatter(Di['longitude'], Di['latitude'], c=Di['h_li'], \n",
    "                #vmin=0, vmax=2000, \n",
    "                linewidth=0)\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('latitude')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib widget\n",
    "for file in [files[0]]:\n",
    "    this_D6=ATL06_to_dict(file, dataset_dict)\n",
    "    plt.figure()\n",
    "    plt.plot(this_D6[1]['delta_time'], this_D6[1]['h_li'],'.')\n",
    "    plt.title(this_D6[1]['filename'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jovyan/data/nsidc/161027313/processed_ATL06_20181019231110_03260102_001_01.h5'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from osgeo import gdal\n",
    "import rasterio\n",
    "from rasterio.plot import show\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot tracks over srtm data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 18 beam/pair combinations\n"
     ]
    }
   ],
   "source": [
    "D6=[]\n",
    "pairs=[1, 2, 3]\n",
    "beams=['l','r']\n",
    "\n",
    "files=glob.glob(data_dir+'/*.h5')\n",
    "for file in files:\n",
    "    this_name=os.path.basename(file)\n",
    "    D6 += ATL06_to_dict(file, dataset_dict)\n",
    "print(\"read %d beam/pair combinations\" % (len(D6)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc0b47c5c0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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39/iKW04IISeHQ+jk3DG4+l30730B8NIeQdS8i923/Z9QKLR3LYuJ7iGBlxpvFSTN2ytuNSGE2EEDTs4dgysvz5XFtTZG9TtA94E0oYkefWKAc+CEkLWDBpycO9Qf5paPGnfg9e6BZj3v8UfhQYPekltJCCGngwacnDv8QX6KUIFABpcAlUMjrvAACODFUFld7G5CCLFlrURsqpobrN+kiC5SDdoG3jcpLF0G0XelEC9StdoqYcskTDFdD1fHfZzKvn7jcYQ7r00vSASV3fsRR5K6mEmCyZjoABD7+/DC8vnBbT0VTJRJCmFS2y5DTW/b3jKKYRO2yYdMz3GZJDW2z59tYpkiyii78yh6fxU94zaY2lTkCeRKbV7mmXFZx6JhD5ycO4L2fWi8/iQQpy9GGdWw+crfhRc10hWinExqAmjrxhJbSQghp2OteuCEnJTm9XejfvNxJNUu/MEWfAkOl+lgE1KdnvNWBeLmLfi7Tyy7qYQQUgr2wMm5xYurqPR3ILO5vvuXMTnqpZr6g2uTSU0IIesDDTi5eHTuSyOwjV3JIGlAl9r+qltGCCEnhkPo5MLhda8hyQRsU/KTymAl7SGEkDKslQE3qdBNasYyqlZXFKk7XalXy6jQbZWwZRTRtqpdW5V9mbjVU9sPdqAKzO5GvRiJJhCIUyW4q5jIRfuxba+tinkZcZ3LxNc3tcv2mSmTP8B2fdO9WaT2Nm1jOle25UVK8Lx37XHb5FEmBrxpG1sleJENsFXHL8OrwhYOoZMLh6gPhK2p3rcCwKgJweINFSGEuIAGnFxIKq//BBTjdKKpAU/6V1bbKEIIsYAGnJxrFIr+9nUcXPsOhs2jVLSy/xDCuIpYPcTqIUwqGG7eQLj9yuoaSwghFqzVHDghNqgkuP3Of4Ph1s3Dss2bj+PyGz+OaOs6EgESnX4ERluvo7r/6JJbSggh9rAHTs4tvcuvTRlvAGjf812E9T3IqJm/kZ8v3CGEkLPGWvXAVTVX2ecqLnAZXMZCdxVTt+i4XcXGLhNn3nb9MkrwyWXDVn5glu6lV7F9/UfhxR4Sf/JcKbyN10vX7UrBbVq/jErbFpdeB6Zy03kqOgZXnhim9YueGVfXz0SRqtt0TkzbuFKnl9nGVo1dpEIPgsC4zIai+7bo2G32ddr31GlYKwNOiBWS/5AMW3cgENT8ISIRxOrBg6IiMaQ2gkKpRieEnHk4hE7OLUHncm55VOsASN3JAklQ9yJUvRieABAgadxeYisJIaQcNODk3FLt5RvwuDJM/xjNZyVTAAmzkhFC1gAacHJuCXqXZmKlZkg6J6eD7dzFEQ04IWQNoAEn5xaBwB/mqM1VoFCgcy8STW38YVAXBZL63pJbSggh9qydiC1P0biMWLQnibF9UmwViGVigtviKmZ20TauYqQXMVt30LmCuD6d+xt+gn7rOoLOVSg8JKoQSdOKJhCg1naqLLVVp7uMR+7qWhTd/7YqdNs410XLbI+jzPG5UqGbyotU6LbvHZex0G23caVaL1NHmWfGVuluepbK5OKweVcUeoAYlxByHvDyH67+9nV4nWvQLJ1orIIEHgAB/OFy20gIISWgASfnGhnVcstHjYOJYC6Z/BxHX9ULF942Qgg5DTTg5FxT7W3nlkf1dvpHnDOLJEDcuDNfTgghZwgacHKuCdpXc8uTQ1eyVu7yqHkzt5wQQs4KNODkXBMMNzCrAVEFYtE04lp/K3e7eIMGnBBytqEBJ+caUR9J4qfuYnqkNFcRjKpdSPeeOV9wBRDRlYwQcsZZKzcyVc2V7C8jaYkrN5YiXLmLuXR5K+Oe4ap+Z65coyqS2vBQpjb2+x7W76LRuYZQBZ4oBIBCUmMf9HOPvei4XSXVsXX9KlO37TUqcukzLXPZJlfHYevyVsQy3Dtt6zCdpzJuZKZlpvLRKD+Tn6m8qG7TcdjW7fL4TG0q89yXcT3Lgz1wcu7xR83UMCMN1qKZ6ny4uQu/cxWJChL1kMBDopKuy7SihJAzDg04OfdUu+N57ml3sbCxDy+pQdVHMu55IzXwCiCp9FfQWkIIORk04OTcU+3t5JbHtW76RxKkBhweYnhpNDYRupIRQs40NODk3FNr7yAvq0lcHQAAvGELgBwOrSObDY82mNSEEHJ2oQEn555K2Mqx3wpIApUEfn87GzQ/+nhQxC3mBSeEnF3WSoUO5Kv3TCpAl2pQE2UU8CbFq63qehnJTFxi294y6s7cOhIPngpUFIqjWXARIKx04B1cgXf1+4c2frw8CbpOPBxcXaeia+RKrWxav6huk6LWpE63TXJShO1xFKmSTbhSmy/jfWT7zJRRgpsU7SYluEsFvKkOU1tN6xfVsehywHyd8s4Vk5mQC4+MqhBgnK7k0EgPW3fgd66l62BS4gZI1FhqGwkhxAYacHIhCIaGkKn1LvzeVaA3H5HN3/2hRTeLEEJKQwNOLgT1/XvzFyQeBILaK+9DnKSPgyoQJj7a116GYvFBggghpAw04ORC0Lr9aG5M9HYrDZk63LqJIQL0kir6WkWECuLGAYbbb66gtYQQcjw04OSCIBhoBZGmAVsiFQw0QG/zLgAgqh8crjc5Ez689Mbym0oIISdgrVToqmqlCj6LymrAbaxyW1ydkzLHvehY4UV4Qx+qHsKZ36wqCUYyhDes524X1vZPrUS39TooE3/etMz2XiuzH9vjK6N0t92XbR1l7qlV5iiwPY4yz56tct1V7PSibWwV7ab1i+q3rcM2RnrRNnnlVKGTC49AgNifGkZXBRIF+rUOKp0rudvF9e6SWkgIIXbQgJMLg4SNNExLZrgVQAIPvc09VNpXkczOkQOIKuEKWkoIIcdDA04uDJXeJiLNMo6pIFYPsQp69Tb8sIkEghhpxrIYQKxZBjPPPAy3DkSJ4q+ud/F/vLiLb77VRTz7S4UQspas1Rw4Iaeh1ttEe+cWEqRR2DTrhfcbbQgEGlegfpyFVR1nJROMam3U+vkJUc46caL4V8/fxOv7RyMJf329i48+eW0pkcEIIYuDPXByYWi1Lx2lCtVxeHTBKBimKwybR7nB9Sg3+LB1d3WNPiXfvtWfMt4A8P3dIV66M1hRiwghrli7HnieIs+lstSEy95KGZWxi/0D7pSwZRSWJmzjdZeJma2qCLrj0KjT9akfI4ojeIMm4kYPOhMVfdC6i9aNRw/3U1RHHqbjsL0WReubzuHN9jC/vBPibTvVE++nzHGbyk3HUaQYtn1mlqFCd0UZ74Kic2VDmetqqttUHob5OpIysdBXqUK3jQ1fhM27gip0QgBUogC5gdUEiPwQQffSYa98bLwVwKjRXlobXfPAVmBVTghZH2jAyYXCi/MHnfrNNqqdS1NJRZPs/6jaX14DHfPYTg0/cm3ax/1v3dfAw9u1FbWIEOKKtRtCJ+Q0VMIqwmB+yKu3eYCrt+4HDiVsR2jFHHDirCMieOZHdvCeB0Pc7Ixw32aA+zfnh84JIesHDTi5UNR6LYSt3lz5oNGGP3oUUwnDM1QUiTeCl6zvsPODW1U8uEXDTch5gkPo5EJR786nDVUFBrU+BB40Z4hdIQjrnWU0jxBCTsyJe+Ai4gN4DsAbqvoREXkSwL8EUAcQAfgHqvoXM9s8AeAPJooeA/Drqvr5bPl/AeBXsu3/tap++rh25Kn0ysSidaX4to0DDdirkm2Vs0XHZqsQL6NadxXj22Xd433V9ltIVFIP70NfcMEoCBHHMSSsIZnwBU8yQdugeRdBe6uUarfMPZJH0bWzVWO7VMCbvAJcxuV2FdPdhEuVvYkyMfVd5Q+wvT+K6japrk2qblu1N7AcFbptbHPbWO9F18iVCt1mCP1XAbwAYNyF+SyA31DVPxKRp7PvPz1T8XcAPAkc/gB4A8BXsu8/A+A/APA3VXUoIvdYtIWQUlT7DUAFmhnvFEHiJ1AovH4TcaMPnRhHVwWGrQNsrqTFhBCSz4l+1orIQwB+FsCzE8WKI2O+DeC4xMkfBPCSqr6aff8EgH+iqkMAUNWbJ200IWXx4wpUx25iE+5iAowqIwTdzbmEJwogbK6vKxkh5Hxy0nGpzwP4NKa9aD8J4LdE5AcAPgfgM8fs4+cA/N7E98cB/F0R+XMR+VMR+dt5G4nIx0XkORF5rtebFx8RYouMgkMXsbG7WKKCsN5Hbf/SYaS2yWhtUTU/IAohhKyKYw24iHwEwE1V/frMok8A+JSqPgzgUwC+WLCPKoBnAHxporgCYAfA+wD81wD+J8mZkFHVL6jqU6r6VLPZPK65hBzLxt3LachUZB8VIBHUBg1U+1vZ8PlMD32VDSaEkBxO0gP/KQDPiMgrAH4fwAdE5HcB/AKAL2frfAnAewv28WEA31DVGxNlrwP4sqb8BdKO0FXL9hNizT1vPJj2wg972YI4qiIRwA/rkGH90GCnPXRB/YC3JiHkbHGsAVfVz6jqQ6r6KNJh8D9R1Y8hnfN+f7baBwC8WLCbj2J6+BwA/udsO4jI4wCqAG5btZ6QElTCAINRDcO4gjCpoB8H6HuC1+5/EwLB1Zf+BjSuIM566N6wgcs/eMeqm00IIVOcJpDLLwH4bRGpABgA+DgAiMgDAJ5V1aez700AHwLwyzPb/w6A3xGRbwIIAfyCHuOXoaq5kv1VJiiwdTkAgEplsfFzilxubF3PyrhImLBNQlLG/cp0HJPnfFgLMapGgFamxsbbzQ6iKEJwexsPHPwk+lduQ2IfjbtXIYmPCFHhuTUtc+UCVSaZie05dJm0x6X746KfZZduZKZnw7b8uGUuKLqnbN9tpqQltq5fRdvYtqmMG5ntdS1TtyubZWVJVPWrAL6a/f1nAN6Ts86bAJ6e+N4DcCVnvRDAx6xaS4gDgrACP/IRV6YfvEb/KGa4xD46tRD7l+/Av/8tXHnrPuzcunfZTSWEECMMpUouHJ56ePAH9+C1t10/LFMFwuhohOCNx17G/rVbAIARhnjj7S8BKrhy976lt9eGt164je/9Xz9AEiV49H0P4NG//eCqm0QIWRA04ORCIsM6OlEVgcRpJLbER3uni4du9rERBti/emtum1sPvn6mDfhrz13Hv/kX3zicFvjB199C7+8P8K4Pv321DSOELATGQicXkr2NPhL1MEwChEnlMPLa9Z0DqJfMJTQBgLA2hJ5hh7Jv/evvzfm7ffN//R40ObttJoSUhwacXEhU8o3afrOPyqiKPC1JIorYNwtTVk1vdzBXNmyHiEdmsRAhZH1ZqyF0VS1U9rmqIw+XalBXx2CbFKVomUlJWSYJgmmZbXvLKOBPWndjrwLkRN8fVEcYjUaQUQUaRIcJT4D0/26jg632Jat2uVJ8F6l2Pc/Dve+6ile/Nh3R+Oo7dqCeziWUsFWtF3kQmI7btE2Z+3bRauwyKvQyHig2+zmuXTbru1Tx2yYzKaNCd5VQpEwdtuWm4yuT0Mo2mQl74ORCstnNz40d+ukD74e1NFpbFk41UYHCQ2fj7KYVffLvPY6dR47SpW5ca+C9P/83VtgiQsgiWaseOCGuaAwCqAKzHVH1AIUi6DUwaPYhOpGVDIJuq7vklp6c+lYN/+6v/R3cffUASZTgytu24fn8jU7IeYUGnFxIBAKJBerroREfZx7r1kM02xvYv7ILb7wsW96rnf2kJpcneuGEkPMLf56TC0sl8mYyj6XJS/abQzQ7zWwIXaAQaJb8JAzy5/kIIWTZ0ICTC0t9EGAu65gC+xshGv0mEvWQAIg1zbSjKoi95Ey7khFCLg5rN4Sep8hzqVC1VXGWUX3aqjgbjUZuua3Kt2gbE2Xie5sUzqa6yyifXdDoBNjbGk65fCcAYiTwIw9QQaKzbRMMZIhqFMztz1XMc1N50T1l60Vge12L6nZ1ncrcU7brl3lebd8vLt8Vtp4NruK2l9nG1mukqG5XavMydSzj+KhCJ+SU3HO7Ac0yjk3+v9WtAQC82ANmc4MLzrQSnRBycaABJxeWrU4VV3YbWc7vLIiZApU47b3VB3muZoreGVaiE0IuDjTg5EJTD/3DIXRB6lb24iO7SEQzAz45fKUQKHobveU3lBBCZqABJxea3a0BBOmDMB4ojyoJ2vUhNtobE4Pr2QC7AMM1cCUjhJx/aPyuJAIAACAASURBVMDJhSZf66TYbwzQ7DQhgqkPIYScFdZOhW6jCC2jAjRhUgLaxj0u2pcJU1srFfvLZxuX21RHUd0mVbJJfW9a32XdpuOuDT306orJ9GMCYL/VxwN3NuCPfMTB9DXe3N+0UovaKqiXge15KoNtzPOium3PoasY4mX2ZascL8KVqtylAt6VErzo3emqjqK8E65ioZep21XMevbAyYVmu1vLhs4V4zluEaDTGMJTDw+8/MBcis6wFq6iqYQQMgUNOLnQbHSqh8Pj3sQw+aCa/tJXT+dyg7d32uhs0pWMELJaaMDJhWa7U5vrYQOpkA0A+hv93O3uXt1dZLMIIeRYaMDJhaYaVXINOACM/BhI8udeu818w04IIcuCBpxcePw8Iy1Ik5octDCrK1EFhnXOgxNCVstaqdA9z0Or1Zorb7fbueuXUX0uWplY1C5Tua16uwy28ciDYD4W+JhqNS+Cmb362DaOd5ltVBXVoY9+JZopB3abPTx0twWFN9NGQezHc9fYVbzuMuvbnsMy3hO2dbu63scts8GlEnzRccqPW+aijqL7wPa4bdXYZVTotnW4ikde1KYycdhtzi1joRNSQLNXneplj/OCt5shasNatmw6a1kiQCLukugQQogtNODkwrPZrqXx1g7zggOAoNtIs8PJyDs08KpHBr5jELgRQsgyoAEnF57t7njIf7qXHVbSoTF/FKTBVDPDPc5a1mFMdELICqEBJxeezW6QK0SPsun/eq96aLiTLOQLIOyBE0JWCg04ufB48IB4Nu9Y+ulVIzQ7TahO5wVXBQb10SqaSwghAGjACQEA+LGfzoEj+2g6TL7fGKDVaRzOfQNHc+CRTxEbIWR1rJUbGZDvmlKr1XLX7ffNQ5yuXCTKuIbYuvDYupcVudzYuou5pMj1LA9Tm4rOX5GLWR7j61ob+AiD5LAbPh4m32sNcfn6ZtoDF50w4gKNvRNdS9P1sC13iakO071WdH+4cpty6Ua2jGQmq0woYmIZLmyu3MVcJjMx1V3mPbwMNzIbO0M3MkKOodULgEycphPD5AeNCJXIR9CrI1Fv4iPo1WJEvjt/akIIsYEGnBAAm91q7jB5r54a6DgRRElquBMVROojEcHBxnB1jSaEXGhowAkBsN2uZL3vaV/wYeZKJipI4CFSH5H6WS8daLcGq2oyIeSCQwNOCIDWcDw/P+0Lrh6gUNT7+fP3bfbACSErggacEGQJTXKmsxXAMEiw0alhVkuiCvRqdCUjhKyGtVOh5ynyTArVIuXscJjfcyqjmMzDVSD7ovXLqHlt1ebLSMBgalOZ4zNxEjXv9kGAvUsjjHevCvgjgT9UNA8q6bD6ZMx0AGE1OfbecKVCL3PctufQZQKgZVxXW1zez6brvgwleJl9uarblUq76Llx5Q1U5j3sSm3uMlmLCfbACcl4x2sbQIKJmOgCP/KRCLDRqaauYzNK9di3N3iEEOKCteuBE7Io6kMPcZL9phWFqqBXU7x2Tx+PvdUEMnHbZA89UWAYxKiNFu9Hvyh6dwd48X/7AfZebWPjviYe//d+CNsPbay6WYSQY2APnJCMg1YE9bIetnoYC9neuJpOt8jIP1Spj5XqCsFBa32FbPEowZ//99/Cm9+4jd6dIW5+axdf++ffRH93fY+JkIsCDTghGdXQmxOqAUCvms5X1YaVNMRq9kmy+OjtzfUVst381l0M9sKpsmgY4/Xnbq6oRYSQk0IDTkhGczg/DK4AYg9IRNHs+piN1gYAB81wbrt1IQoNApwhI8wRctZZqzlwVcVoNN/bKaPos1UgmmLt2qoGi+ouo6R0VbepDpMyuCiueaVid1uZ9mUqL1Irn+bcekkW41xS8zzeIoGg60fYOAhw894BJmtXAN16jDiOV6o2t2UcM/7aj1yCF3hIRhPXX4B7f/Tyie872/jzpmfMdj9lcPm8ulJEl3lPufIuWIYKvcxxL0OF7qq9Lo8vr02MhU7ICamER8Pjk//vbsTYbFenM5YhzVoWBuublay2EeDHfv6daOykCYGqrQre/fcew/bDFLERctZZqx44IYumMfAxrMZTSnMA2N2I8MDdejp0PuMLrmv+M/jaD+/g3/5HlzBsj1DbDOD5a35AhFwQ+KQSMsFmN1WfTyY2AQQHzRgePCCe9wVPh9Hzp1jWBfEE9e0qjTchawSfVkIm2OmMhWzTMdH71XSYvBJNK9XHWcvarfVVohNC1hMacEImuNTJD8gSZnq6+mDeFxwQ7K2xKxkhZD1ZqzlwVcVgMJ++0aQmzFOsH7csDPNdgkwqdJeKWlfxjYswqR9N7TXFsy5SmtuqqE3n0FR30f6tYwnPHHdtiCzc2sx+BRhqhFbbx8HW9L2gCrRrkbWS38Qy1Omme8p0H7hUK5vqcOltYUvR8bnKUbAMFbpLTxZXiu8ybbI9V8uIt76Mum3f9eyBEzKBpwKZee7Gw+TdWoKNTpD2vHV6Wa/BmOiEkOVCA07IDM2Bd2ikdWKYfBQA2wd+ltRkIpyqCsLK+rqSEULWExpwQmZ4+EYFsxHXVIGhp2iE/qF/+PiTupIJEmEvnBCyPGjACZmh2fcQJzI1TB4lHl6+N06H2BNgWqUugKQR2QghZFnQgBMyg6dArB5GiYdRnP6vEOxn89zVcF5Mpgr0ajTghJDlsVYq9DiO0e1258ptFeKAWYVu2pdtuUlBDbhTE9vGQwbMCmCTqtxUXhQL3VS/qW7bmMRlFP42ytJ6Z1wmk0HXEHnp+jt7Aa7Xw7lobUEoVmrfMorv09Jq9PDQPTex297Grd3LJ667TPx50zarVJubKKOyd6U2L3pPrapNLuuw3X/RvlzFYS/axrZ80bHei87TWhlwQpZBLfKAJJ4bn1IBBpUEj7xRxY1rIZKJ32jbBxVc6pztx+nHf+QFfPC9/w98P31JvPD9R/G//On7s9znhJB1g08uITlU4slQqkd/7zcSNAc+nvrrDWztV+BFHvzQw+Ze5UyL2DYaPXzwJ/7i0HgDwI+87RW867GXV9gqQshpoAEnJIf6MB3+PYqJnorVdltpebuhuLMJDD0Pg4qH7z8U4VuPDlfW3uN46L4b8L35HxiP3H99Ba0hhLiABpyQHLa7aRrRyXjoqqnADQBeuX9eQ/H6tRFG/tnshe+1N3PLdw3lhJCzDw04ITm87ZaXBWmZDuhy70H6yAxqOYZagPYZdSV76/ZVvPjqw1NlB90m/uo7T6yoRYSQ03K2VTeErIidruCB2x7euKIQSQ14kgiubwDbfcAfCVCdNuKqwN2NBJfnHSXOBF/5k5/Bu9/5PfzQfW9hr72F57/9w+gNGqtuFiGkJGtlwOM4xsHBQW65af2ifdmU27pImNzLlkGRC5vJLaxarVqtX+TaYKrfNjmJy8QMtnieh73ARxTLoQEHBN+6N8HbryvqXaDbzAbYs+VxIrjTiPG2mXvI9vjKcJI6YgDPv/B2PP/C2yfWmL5PTfspuqdsk7i4TMqyDGzdqUzPfplkRa4Sh5jKi96RrhKplLn/XbmLubQBq6q76DxxCJ0QA506kIZRPZoHj3zBwAe2OoIk8ZCoIE4EiXoABO3m2ZwDJ4ScP9aqB07IMvFjIJ7peKoCuy1g58BDojFkIu+oqmBgjm9DCCFOYQ+cEAP1cN4XXBW40xRsd5EmO9GjDwBE5pFmQghxCg04IQY2ezqlQh9ztymoJB6Q4DDUqmafBIJecPZChRJCzh8nNuAi4ovI8yLyh9n3J0XkayLylyLynIi8N2ebJ7Ll48+BiHwyW/bfisgbE8uedndYhJyey91pA55+BJ1q2tv2oyylKDLjrUdBXgghZNHYzIH/KoAXAGxl3z8L4DdU9Y8y4/tZAD89uYGqfgfAk0D6AwDAGwC+MrHKP1PVz520AUmS5KrQy6gAF50MoCjZhy0uVbsmNbFtYo1FJtwY41JtXkbpvnUgUFSAmVX6QZoMZ7MruHvpaPg8qwlRkiCKjjYynduzqMYuk4DEVm3u8nzYblPmfra9D23fFctIKFLGo8PVu9Bl8iHbNhXZgEUnTDElzCpTh4kTnVkReQjAzwJ4dqJYcWTMtwG8ecxuPgjgJVV91aqFhKyIrYECKtAsGptmn8hLe92Pvq6YzQvuxcDV+d+YhBDinJP+NPo8gE8DmPzZ8EkAvyUiPwDwOQCfOWYfPwfg92bKfkVE/lpEfkdEdk7YFkKWQiUBJLvjD+e6s8nufiC4tgts7x3Nj6sCoXp47crZ61kTQs4fxxpwEfkIgJuq+vWZRZ8A8ClVfRjApwB8sWAfVQDPAPjSRPG/APB2pEPs1wH8U8O2H8/m2J8Lw/C45hLilCDKE7IJbm96SATYbXiIYg+jKP1fIXj5XhpwQsjiOUkP/KcAPCMirwD4fQAfEJHfBfALAL6crfMlAHMitgk+DOAbqnpjXKCqN1Q1VtUEwP9g2l5Vv6CqT6nqU6ZoYYQsimY/HUaf/KgCdxseVIDYA6aH0YERXckIIUvgWAOuqp9R1YdU9VGkw+B/oqofQzrn/f5stQ8AeLFgNx/FzPC5iNw/8fU/BPBNi3YTshS2+0luXvDdpg8/Ae7ZmxfVXNtfUuMIIRea00Ri+yUAvy0iFQADAB8HABF5AMCzqvp09r0J4EMAfnlm+8+KyJNIpxdfyVk+x2g0wq1bt+bKyyiiTepVU+xvk5LSVD4cmnND26pwbeNTFykZTcdnwqV61ZVytkh5XEbxWsSlPUCvVTEpRVcVtANBFEV4/HsRbv54E/CzhCcq+MGG4Im4j2pc3CZblbZLbO+1onNuexxlrqsrXHpVLEMpvei6y6jvy6jp8yhzvZdxfK5inhfV7SoWutXbXFW/CuCr2d9/BuA9Oeu8CeDpie89AFdy1vtPbeomZBXs9MY98MmQqUDop8bpzlaAUH3g0G1MEAfAK9cqePyt1SW0IYScfxiJjZACNoZpuLVZIZtmrmS96tiwH82BA8CNS0wzQAhZLDTghBQgSJOaQCeEapmQbb/hozmYniMHUiPfqfLRIoQsFr5lCDmG6kgP/b+RhVMFBLebPnbayXTPPFtn6PPRIoQsFr5lCDmGjX4MYDrrGBTYbVXQChNoIqlhz4KiqwpiAWK6gxNCFsjaTdRF0bwwyGVs5TJqxkVjUo6bjq/IX950rmxjujcaDWMdJvKuXVGbbNX3gPk4ThMze6szws3t6sxyYK/qIQlHkFgwW6uqYF8jbISJdfz5VcYKL1O37T21yhjwZVTottu4iiFeZl+2ivaiul2p0F16CZko4y1j216XCnhXzwB74IQcw+XOaH6YHEC/lhrmSqjQxDvKDZ54UPWw21q738eEkDWCBpyQY9juRZmIbSKcqgpGWc+z2Y8PDTc0M+QJsNtyl5GOEEJmoQEn5BgaoyQTsE2EVAXSzGQCbPSio/nvcXJwAHtNGnBCyOKgASfkBHgzSU3GwV12WwEud6J5X3EV9OhKRghZIHzDEHICGmE85wsOAHebVVzqjY7KJnroYYWPFyFkcZwLlY1JBVgUU9qkArTdl0n1WSZ2dJG6Og+TQrxoPyZFe71et6qjjHLWVq28jJjgJ6XZj9BpBJMh0QEAd5sVPPZWmHa7Z65r7HkI4wgVw/HZqtPL3FOu1LxF+3EV27xMHcvAlbLbZV6BRSuoi5bZxgq3jZFehKv7GXB3Dk0sw9vi7LwhCTnDXO6Ec8YbKuhUA3gKSDKZL/wooEu3ei5+IxNCziA04IScgJ1uCCQzw+QKDIO0Fx2M0l/zk+5mgODOJnPYE0IWAw04ISdguzdKe+BjlXn2f5SFTN0cRDPGPR0+22vRgBNCFgMNOCEnIEgUcugDPi4VKIBhxcNmb5S73QFdyQghC4IGnJAT4scJjtKGHqURvduo4srBMHebfo1z4ISQxUADTsgJaQyjeV9wBfaaVWz38+K8C0JmJSOELIi16h6oaq6U39YlrAhX7mVFrlymOkz7qtVqueWmpCVlkpmYXCRMLiMuA/XbupetilZvhINm7ajznZ2C3UaAx9/MVOqzxy6CYRKjknO6XN5Ttti6RZZxyXRV7pIy7miuXLNs3dHK1GGiTJtcJWUp8w5ZhsuWy+Qkq4LdA0JOyE53kP4xzg2e0a2n89x+lByVT6yzTyEbIWQB0IATckIu9YY5vuBAWEl7xrXMlWx2nV0acELIAqABJ+SEHLqSTX6QRlwDgI1+XrAX9sAJIYuBBpyQE+IBEFVMK9EFEEGvWsGlTpiuOGPg23W6khFC3EMDTogFQZwvcNlrVHGpG+YuGwRrpRUlhKwJa/dmyVM0mhSyZZJh2CYaMZWbkoYULbNVlZv2U1S36ZyYtimjgrVVutuqO4vqdqVkNu2nOYgQzhpkBXZbNbzjxl6mRJ9eFvle7n1rqqPMebJVdrs857a4TNayDFyp0Muom10lRjmLdbtUwJfB1vvF1XEX7cv2uWQPnBALtvrD3Hnwg0YV1bEKfWYZAIT+6gwQIeR8QgNOiAVb3eHsDDgEQG/sSjYXrS2dI99v5adrJYSQstCAE2LBTjc/ZGqYTaU0hvMR2VTpSkYIcQ8NOCEWbAxH865iAFTSxCat/mg+LziAvQ32wAkhbqEBJ8QCASBJzly3CDq1CnbaaQ99Oi840GVSE0KIY87FW8U2tnjRMldq8yAw+/6aVOWmmOemfZVRoZuW2cbALqMYtlWQllEeu4q/XaQSrUYxhtX587LfqGK7259XogMYBv7cPm1VrWW8KspcPxfrl6HMPeWqXS7jctsqjJcRI91URxmltCu1eRlFeRnFtwlX95RLlX1eedH9xx44IZbkzXMDwG6rga3eKOt+Z4VZDz3xvLyRd0IIKQ0NOCGWbHUHuSFTO/UqfNUsWhum1xFBj8PohBCH0IATYslWP1Oiz/h792rpVEcQ5Q+p3d1sLL5xhJALAw04IZbsdMY98Gl/71Gmk9joj+Y3UmCvma9xIISQMtCAE2JJfZQzB66pK1ksgs1eOB2JbRytrUUDTghxx9pNyuWpasuoCW1jgpvKTep0k9K8aJkrtXnRcbtSOBfVsYw4xra4UmMfbpco1JvZVgTtiodLex28et8lpFYdGEvS+0HlVOegTAz4VZ5zEy69C2zv27N4PpYRj9ykiC6q27SN7b5cqtBt91Umf4Dt+mXyDbhS07MHTkgJqmF+L3y/WcfWYV7w8fA6DpOaEEKIK/hGIaQEjWE4rTIfC9nqNTTC6MiVbHIo3fMQrzCrFiHkfEEDTkgJrh705w20ZlHaAFSiZC7hCRToNhgTnRDiBhpwQkrw4J39+V62Au1GGvN8e9ZXPPu7EtlH3SKEkDxowAkpgYzjpc70wnc3WwCAx968M23gAVzb7aCZN3dOCCElWDsVuo1ysGhdk+LbVgluWr9eN2efslW0m5S2tjGai7BVtZZRoZsoU4cttnUcV7fE8WESk6l6BBhAsbXfxY+98Bq++8i96NeqCEYxdu60EUU04GU8AkzblFFX27KM+PomXKnKy8Rht1Whu1LMl9nGVF70vNmeK9P7uQym+znvOBgLnRDHCAA/nnnYFIAKullSmv1GE91qE4lWMKzU8N1HHsRr91xZelsJIecTGnBCSlILo7khdAGwt9EEAPzg3nlj/cr9V5fWPkLI+YYGnJCSNPtDiGLqAwCdRhrzfFSZH3KLfJ+uZIQQJ9CAE1KSzV4/t7yb6R/mhtgBAIJOgyFVCSGnhwackJJcandz04oOK6mwsRqO5l3JFDhoMisZIeT00IATUpKN4RBIkH706P8kU5g2hmE6rJ6tI5rOkR80m6tqMiHkHLFWbmQikivlN7lllUko4qq8yOXA1j3KOsB9QYIHW5c0U1uL3E9WiaukJSd1BZJEAc+b64l3VLFx0MXu1ta0p5kC3VoNURSVdmGzoYzrki22CUVs7zXgbN5vpmfc5fPtymWrjBuZqzrKJO6w3cbW5Q0odjHLYzTKSROMcu5lNvcI3cgIWRCVUTw/TJ6kQrbNbm8uUhsADKv5sQMIIcQGGnBCTkEtHB0No2dD6ALgYGMDm93ukUIdR0r12FurgS9CyBmFBpyQU7DR6827kinQqzcQxEkaThWYSHiSDp2FhmkfQgg5KTTghJyC1tiVbGaYvF/LXMmiJDXa4w8AQNCmkI0QckpowAk5BVudzpEKHTg04pGfznPXh8P5eXAFDTgh5NSs1Tie53lo5rz4TCpAkzodMCchqdXyg2yY9mVSExYpLF2pjF2qlV0mYHClcC5zfGUUzqchGPYhyOz3OJyqAioeRkmCRreHbrOVLTha56DRMKpayyjpbe9Dl54Ntte7jPre9v50ie29Y6tKLpNQxNV1LaOAt02kUqatJoW4y/Nhq2g3YfscF5F3r1GFTsiC8JMESHQunCogGAZVbHY6R2UTYrZhzZytjhBCTsJa9cAJOYsEoxFG1ZmRGwW6jRY2ul0Ak4Y9ZXQORGyjwMfte7cgIrh64wDB6Oz5ahNynln/twghK6YWhhgFtbR7DRwOk3ebLVzZv5sq0WeGxlTWe/DrYLuBbz31KOIsYcv3H78X7/76q9jaz48PTwhxz3q/RQg5AzT7aS97Nq1or9FMh8zjZE7kBgh6azyM/v0n7j803gAQV3y8/MR9K2wRIRcPGnBCTslGr3sUrCX7QIFhkA6rB1E0ZdzHf3fXWIne2Z5PyNLZYpIWQpbJWg2he56HVquVW55HkQrdtMxUbquwLMJW1Wqqu4zK0YSrNhUtc6UQL6ModxV3PO8YGu12tnC6PKoEiOMY1cEQYTA9Ry4A2o0NXLlze25/pnvK5fW2VW/PKnOb7T662825stPELC86PldeBGVU664U8GWunytld5n3l60K3bbuMup7kzq9zPG5irduouietbkXqEInZIFUR8OjiGsTqAgSETR7valQquNobYP6+vZYH/n29XRqIMOLEzzynbdW2CJCLh5r1QMn5CziqUKSBDrp/5sZ6WG1hla3g1vjKGwTvuDr7Eq2faeDH/vTb+P2A5cAEVx9cw/1Qb4/LCFkMdCAE+KAIIoQetMBPEQE3dYWNjr7qdGeMN5ptLb1fvxqgxEefPnWwoLkEEKK4RA6IQ6oDvpTIraxkK1Xb6EWhhDVnNSiAgWNHyGkHDTghDig0e/O5wUH0Gu0IFAgSaazlgGACAazAWAIIeSEnHgMT0R8AM8BeENVPyIiTwL4lwDqACIA/0BV/2JmmycA/MFE0WMAfl1VPz+xzj8E8FsArqnqvCR3en+oVqtz5SZFXxnVp0nluE4UDWnaKkJPoyo+S9jGF7cdFm5127gNzCnRh9UqkiRBZTRCVJuJka1At1ZHdexHXrLuMriMfb/oul3isg7TPWWqwxQj3WWbbNXYRc+3K1V5GRW6bSx027oBdyp0l54Ni4yF/qsAXpj4/lkAv6GqTwL49ez7bMXfUdUns3XeA6AH4CsTjX0YwIcAvGbRDkLOHM1ed854Q4HES38j18Jhbg+929pcSvsIIeePExlwEXkIwM8CeHaiWAFsZX9vA3jzmN18EMBLqvrqRNk/A/BpzL/6CFkralGYupLN5AWHeIg9D41efrS2fmM+rgEhhJyEkw6hfx6poZ3sLnwSwB+LyOeQ/hD4yWP28XMAfm/8RUSeQToc/1fHpBH8OICPA0C9vr5uN+T8U+/3MGi0pmOiawJJFM1eN1euFhrS1xJCyHEc2wMXkY8AuKmqX59Z9AkAn1LVhwF8CsAXC/ZRBfAMgC9l35sAfg3p0HshqvoFVX1KVZ8y5fAm5Cyw2d6b6oGnanQP+9uX0ey2c8eZosq8poMQQk7CSYbQfwrAMyLyCoDfB/ABEfldAL8A4MvZOl8C8N6CfXwYwDdU9Ub2/e0A3gbgr7L9PgTgGyLCbAhkrZmMuDY22PtbV9JobbMWPHMlS+hHTQgpwbFD6Kr6GQCfAQAR+WkA/1BVPyYiLwB4P4CvAvgAgBcLdvNRTAyfq+r/C+Ce8ffMiD91nAo923aurEw8chOuYoKXUYLbtqmMWtl0rlapfDbhUt1pUpC6PG4vHOaWD2oNJKMRkCTATLAXiKBfCVAfHKXhtFU3F7bJ0hOjjArd9vlzeT8vOu5+mbpdrQ/Yxx1fhoL6tHH0xxR5/NiqzW3PU9Ey23Po0lMn73ktOt+nCQX1SwB+W0QqAAbI5qlF5AEAz6rq09n3JlKl+S+foi5CzjzNccQ1YCrqWpQlMvFHI8Q5rmS91taUASeEkJNgZcBV9atIe9xQ1T9D6ho2u86bAJ6e+N4DcOWY/T5q0w5CziK1YR+iChWZUpqr50MB1Po99Kr1iQXpp9fYxGXcyN8pIYQYYCQ2QhzhxxGQBUc9DKcKACKIKwHqk77iE6NiwyZdyQgh9tCAE+IQL8rJyKVAr7mFVnf/8PukUn0U0JWMEGIPDTghDgkMEdf6zU3UM1/w2R56XKF7JCHEHhpwQhxS689HXAOAQWMTwWiYRmubxfMQG+JkE0KIibVLSJwn2S+TtMTEKhMtmLBNgrAM15AiXCXKWOU5L3MfiAhqnX3g8v2YCrumQBjUkMQxJI6gOT3uflBDc9QprKPIBcr0DNi6z5nKy7jjrBKXbmS2yUls91NmG1fuYkXXzlXSkjLHbZtIxdYlrGiZq4QpZdzLbN2k2QMnxCGN7kH6x0xM9DhII65VDe5i6q/db2lCyIqhASfEIdVhP3eYPHUlE1y6+cb8RgpEnAcnhFhCA06IQzxNILNDXllPfBTUjkRuOr1s9+qDy20oIWTtoQEnxDF+FM0NoYsKBs1tSBIfxUlPcOhKNqzTF5wQYgcNOCGOCfp9SHKU1EQSAEmqRK8NehOZyjKtmx5FayOEkJOyVsoZVbVS6ZVRx5rUvMtQ2tqqzV0et0ts1bkukwGYcKVoP4mKOei2MWxdms4LDmBY24AOB1lSk9n7TDDwfFRHobGOovO66mueaQfqLwAAIABJREFUxzK8CFwpn5ehEDe9W4ruKVdeMWXeFa7qWIY63WWiEVcJZFwlUik6T+yBE+KYem9vbggdCoyqDQCANxrNzYEDaQ+dEEJOCg04IY6p9vJdydRPleaVcHC0fILB5qWltI8Qcj6gASfEMUHYg6jOh03N5rmrvXbudmGdPXBCyMmhASfEMQKk89w5MdFHtRbq3YO53vfkEDshhJwEGnBCFoAXhekfszHRm9uot3fTYC8z8+BJpbrkVhJC1pm1UqEDQBRFc2WVSv5hHBe3Oo9lKKJN2MatdomtItRlm2zVq2VwpYQ96XFX+l2Esz1qBcL6Fpp334QkgHqY8BMHFB5GcYLAt/eEOIsx/G0po6S3jd3uUq1vUpXblpd5llxd76L1Fx2PvIxK2+X9nGdLiuo2lZv2U9RW27jqJtgDJ2QBBP32tIgt+4T1jcNobYe+4uOALiIcRieEnBgacEIWQNDdy50DT6pNAICMwlTcptMBXcLm1nIbSghZW2jACVkAtf7+oVE+HCYHkGRZx4JBf97AKzCkASeEnBAacEIWQCVMs5JNuZEBADwoZH6IPWPEYC6EkBNCA07Ioojj+XlwEYzqG6h199J1JpYJgCjgHDgh5GSsnQo9jzIqR5Mi1LYOl5jU9CaVo23MccCd6rro/NmeK5dxjFd5XWfr9qLh4ZD5IQoMapsIuneRd2aToFpKMb9o1e4yPCFceoDYKqXLeKy4ioVehO3zt4y8CbbxyF2q0E0swyvGlWr9uGU2sAdOyIKoDHvzAVsAjJqXEAzTIfY5JB1iJ4SQ46ABJ2RBBL399I+ZYfJRbQOiCaCGX/oBA7oQQo6HBpyQBRH0DubjoQNIallWsmiUs5Vg0GRSE0LI8dCAE7IgKt27ucPk6qc97MqwPy9yQxrshRBCjoMGnJAF4UfD/AUiSMRHZdDJXTxs0BecEHI8a6VCV9VclWUZ9aorxWsZla9JMWlSOdoqTlcdO93UXlv1aplz60p17azuOAbmvAsEYdBA0LkLXH14bpOo2sg9V8vwhFglLq+3y7jcpvvZNneByWvEZSx0E65yAZTZxvYaFW3j8jhM19V0nVzeUzaK/cJ3rXEJIeTUSDjIjYk+alxCtXeQu03sU8RGCDkeGnBCFkhl2M2d545aOwgGnXxXshL+woSQiwffFIQskEpvJqkJ0gQmcX0zVaUnOUNpIghrFLIRQoqhASdkgVS6u0dZx7IPAMRBkSsZMGwyJjohpBgacEIWSJDTAwcA+AEAoDI0KNFbOwtsFSHkPEADTsgCkThMI67N5QYXJH6A6jha2wz0BSeEHMdauZEBbhMeuKCMW4PJfcFVgoIibBMq2LqEFWGbJMCVSxiwHNc6Y91xDK14R3nBFYAKRkETQWcvd5uo2ljovW57r5XBpSuQK7epMslMFu1yWubeNF0/28QrZRLk2J7DMm5WLq+fCdvzbptYpkzCJ7qREXLG8ML+3Bw4AIwal1Ht7hmita3db2tCyJKhASdkwcignetKNmpsw49C01aIPft0sYSQiwMNOCELptLPn+eOq5nSPM5PahK2mNSEEGKGBpyQBVPp3JlXoiuQVNKIa/5gkCNyA8LG9lLaRwhZT2jACVkwlWEWMnVmCB1eJVvemV6eMWJaUUJIAWullFFVo2I5D1vFdREuE0mY9uUq2UHRcbtSwpraCrhTr7pMcmKrIDVRdB8Yz8loCEmS+RCp4iGUCoKDOxjuPHCUMBxI46UHzalzUHSNbJXSrlTMRfuyva4uWcazbyp3WbfpepjOocv72Rbb59KVSrtM+XHL8liGJ4v1u35B7SCETJLki9Wi2lYa7CXBnNBNg9oSG0gIWTdowAlZAhIOcsvj1mUE/TZEFZJkrmbZ/+MhdkIIyYMGnJAl4PfzA7aM6tuQJIaMhx+ngr0AcYW9cEJIPjTghCwB3xAyNammIVMlGuUEexGMmoyJTgjJhwackCXgd2/llmvWw/YMecPDJl3JCCH5rNUkm0mFvoy4zibKKGpdxSM3KRaL1JW2+zLhMo6xrdq8TPxm03GbvBrKnCfTcVQqFXhhLw2ZOrm9AhAfcaLw+weIW5fnth3Vtk/keWF7zou8CGyxvZ/P4nNZ5p6yrdul4tuEK0+WIso8l3mUOR/LeE8t4/60eb8UHTN74IQsAUkiINF5X3ARJEEdQefuVJx0IB1K11pryS0lhKwLNOCELItomFscNy6h0k3zhk/NgyuQ+NXltpEQsjbQgBOyJLxRL7c8auzAH7bTIfaZHroIE5oQQvKhASdkSXj9g9zyuLENQZo3XDQNyHakRhdEta0ltpIQsi7QgBOyJPz+3dzyJEjnuSUKp+fHs/8jxkQnhOSwVip0YDlKTheUUbXaKqhN5WXilJdRtNtiGwvdVlldtI0rpXuRItR03sf78rq7+cuDGuI4htdvIw6aMwvTIfb49veN9Ra19ywqvtcN23PoKi49UC42/VmjzL1pOoemcpOnRpk6bN8hZeo2Yesdwh44IUvCG3bSee5ZxIdCjqK1zcyDx3UOoRNC5qEBJ2RJiMaAGkZfggb87t10/nvmo1W6khFC5qEBJ2SJSJSXlUyQtK7A7+3O99AVgBcso2mEkDWDBpyQJSKDznzIVAXixg68aJAbThUiUOGjSgiZhm8FQpaI198/dBUDcPh3Us+SlsSjnK0EcYNJTQgh06yVCl1VcxXIZZTSrmKCl1FK28aOdkmlkn/Jl6Gctb1Otqp14GwqoifPh7RvAVffkf49LlQgqTQQRREkHED96tHCrCc+qm4BBzeMdSwjDrUrlnH/n0VvFVvvE8D8LNk+Y2XOuat9mfZTFOPftMxWCV50bm2V7rbXr8x1tX2O2QMnZIl4vbtAgqMh8iT9SBYyVYadwzCq0AkhW+vKilpMCDmr0IATskRk1IWoQpKJuOcA4FWg4sHr3p2KiT425EltY7UNJ4ScOWjACVkiAgBJNtw3OVqmQFLbTHvo42WTMdEDupIRQqahASdk2UTDaeOdoc1rkN7dtIeOaV9w+HQlI4RMQwNOyJI5TGoyOdetgDZ2IHGY7wsOgdIfnBAywYkNuIj4IvK8iPxh9v1JEfmaiPyliDwnIu/N2eaJbPn4cyAin8yW/Xci8tdZ+f8uIg+4OyxCzi4y2J3K+y1je13bzobY43lfcAiUWckIIRPYuJH9KoAXAIzfIp8F8Buq+kci8nT2/acnN1DV7wB4Ekh/AAB4A8BXssW/par/TbbsvwTw6wD+8zIHUSZovMlVwJUbWRG27hm2rmpFbh6mZbYuWy7d5GzPYZlz7iphSplEMbMuLkn7JnDtXcD4Xhu3IWim+xj2gcZMb1uBpL4NdG9Z1W2LSxcv07NUxtXP1XPpMgmI7TkvkyDHFtvnu0zdtsdt6xJWtI0r9zLA3v3L1u2s6F6zcSMr2s+JnlYReQjAzwJ4dnK/ODLm2wDePGY3HwTwkqq+mjVqMjlyC7mzgoScP6S/lynM9dB4iwKQ1GjLsD0ncAOApHl5qe0khJxtTtoD/zyATwPYnCj7JIA/FpHPIf0h8JPH7OPnAPzeZIGI/GMAPw9gH8DPnLAthKw1EvWhqhDM/Ar3PCTiA71dyPaDU1PhAgB15gUnhBxxbA9cRD4C4Kaqfn1m0ScAfEpVHwbwKQBfLNhHFcAzAL40Wa6qv5Zt/68A/Iph249nc+zPncUoW4SUIi9kqgJoXIb0704FcZHxskpjmS0khJxxTjKE/lMAnhGRVwD8PoAPiMjvAvgFAF/O1vkSgDkR2wQfBvANVTXFgvwfAfzHeQtU9Quq+pSqPmWb7JyQM8uonztMjvpOasDHZVNJTXj/E0KOONaAq+pnVPUhVX0U6TD4n6jqx5DOeb8/W+0DAF4s2M1HMT98/s6Jr88A+LZFuwlZa2TYTv+YDNYCAI0rkGgIRIP5jcaGnRBCcLpkJr8E4LdFpAJgAODjAJC5gz2rqk9n35sAPgTgl2e2/yci8gTSaNCv4oQK9CJV4VmiSM1rHbDeUeIVwF5t7lJl74qi/Zvaazv9UmY/pm3yrp90bsLfeghT0+AKaHUTGkfAG98AfujvTCvVo6iU4jUP22QYZRS1JmyV0meVMkksXGHrseJS6W4aCXXl6QHYK75dJjNZtDrdlqJnz8qAq+pXAXw1+/vPALwnZ503ATw98b0HYC4Tg6rmDpkTchHQyZCpgqmQqQpAwgGQACpHKnXZehDaugfo3lxFkwkhZwxGYiNkFfTvHPl/T/7AHodMbV2FAPA0/Rz2n3YeW14bCSFnGhpwQlaBKWSqAshSi+ZS31lkqwghawQNOCGrIh7NZx2DAI0rQOfWfGgjBSRoLrmRhJCzCg04Iasi7OTHRK9fBQZ708Z9/BEmNCGEpJxGhb50VDVXCehSYWnCVlFY5LPuqr0mJWqRAn4ZyllX8bRdxjw34fJ82F4Pr78PvzGn7wQalxHf6KKSJMDMtiIeIvWBJJwqX4b62ITtcbu815YR89yE7b1TJneBbTx50/plPFlcxbK3VXsD9sr1k+YhOMk2tnWXUdnbPAOnjoVOCHGPdm/mD5NXsxQD8SjtmSP7jFOPNjgPTgihASdkZWjvdu4wufj1dIWwN78cgDTvWUFrCSFnDRpwQlaEDnYhqke97PH/XjqzlczOg4/XyRt2J4RcOGjACVkZCiTxdLzzcS+8ugX07xwZ9gmR2+EQO/n/27vXGEmuqw7g/1PV1Y+Znd2dnZ3Ztb1RbMeSIQEpJlYUHoLg8IpxzEtIIUCQEAT4RIIEyApIPCUSgpSESKAoCBGRBAgQJSJKQgJYMgEDTpyYBNsJMbZje72z73n0qx6HD1W93T1dVTu3fLu6aub/k1o7Uz1Vdft2dd+tqnPOJTrUOIATLZAGg+QHTNVEl85JRLspqWQAnAZTyYioZlHoQHpEYxmRtqZRrXmRmlkR6qbRuVkRlkUi4E2jefP63DRK1fT9y3svbEaKmioS0a79q3D25nYrIO2TiK4+hXGt1fFzcFwEQYjU0X2fbM7sZ9qHRaKxbe27CFuZDUXq69uqM28zS8G0FrpNWe01XQ6Yvx+mtdDzjk2T2u2MQieqqKh3MTVQzWmtApE/fYl9dIYuDuBwalGiw44DONEChbub4wIuGN/rdrwj8fNXnhynkSWPcHcTiOoxKx8RzQ8HcKIFivrxGfhUJTYFROJ66P7zD43vkwOAKtzGUYi3XH5jiahSOIATLZD6u0CkQIQ9l8odAAKnuQJHmkCoQKiQCJBGC97ayxbZbCKqAA7gRIs2qrgWJQ+Ng2+c9hokiTgfXT4faRw5s5CmElF11CoKXVVTo/fKiEI3lRflmxW1aBpBWiSat9FIf8vLiOY1ZaumOpDd5zaj702NjuUo6MHdO4WoAmiuIuhtorUnEB0ApNGZaqNpe00zBfKYHrdZihyDpvso8v7Zqo1dpM9NswVMa6QXyUYw/SwVidK2FW1epNa7rayYIrXeTY9nnoETLVg02ErNCGt01qHDrXje8L2FXqSBmVGdiA4VDuBECxZ0N8eD9LWHwvGOARoCURQvS+6TX6vIxoIuRIcaB3CiBQv7l5KSqTr17yiVTMPBVBoZEF/mc5dPL6S9RFQNHMCJFizoJWfgwPhfAI4T3xePhtup67ltTmpCdJhxACdaMA16KQsBQABpIBpcTl3P5axkRIcaB3CiCtDQTymZKnBbqwi651LXcbyV0tpHRNVTuzQym+kvaUxTC2yl0ADmaWFZbcpLXzBNASmS2mMr/atIqoXpOmWkU+1HFHTR2JtKBsBprsLffW5mThMoIG7r2uvKep9sTVqS1+emqUtZ6jaZiemxU+Q7wdbxmZU+WmT7We+T6WQ+Nr9bsl5fHlvpYqbfz6b74GQmRBUXDrdSl7udDUT+Vvwh3nuG7jCVjOgw4wBOVAFBN2XubwXc5vH4x8hPXc9pn5hzy4ioqjiAE1VA0Dsf/7B3WtFGPGmJBv3U9dylU/NvHBFVEgdwogrwe5up1djGqWRXUtdrLG3Ms1lEVGEcwImqIBoCGk0P4kkqmTgtBL2Lqau5zdUyWkdEFVSrKHTALGrS5mQYpooU0c+KpLQZ5ZjFdGKGIttaJFuTB+R5oa9bIx/itqYGcRGBeEfh75xFZ+OOmXXEWyo0aYKtvy+yrSLZE7beP5vHZhmZDVlsZqZkMZ0Ix9YkJ3nrmCoyBphmHGWxlS2T9z3PM3Ciigj93amSqQIAqvA6pxD1L01VaQMAKOA4XuntJKJq4ABOVBFB/3IyqcnEA4DbWoOG/dkJTwDE1do4iBMdRhzAiSrC711IXd4YpZKFw5kzdBFBY2m9rCYSUYVwACeqCL97Nj0XPJk2NBruzK6kCrdzcv6NI6LK4QBOVBHB4NKeOcHj5ZJcIg+HW7P3wQF4HZ6BEx1GtYpCV9XUaEqb9chN691myYsczIo2z6pbnbU8q015bTWNRs2KFM3bh81IZluy3g/TGvA2o1pn2hL5gIYQuPH18VHTxEGkHvzuObSO3TIziDve0cxtVjEjIIvN49Z0O2VEpxf5XJhGlWf1YZHIeNMsl6w2mUatA/b6sMjn1TQbKOv72fOyY1OyXrvvz1Zc3NpKL7MM8AycqFKiMPkA78kHb7RWEfYuphZ7cb3lUtpGRNXCAZyoQiJ/N3WQbnZuQNi/MI5EB65dZhemkhEdShzAiSrE7yeR6BP3wUUVXvNEcoldx89jNBeZxAVgiOhQ4QBOVCFB/yIkUohqnCaWnHF7SclUjfzpQi8ABAK3tbagFhPRonAAJ6qQ4SiVTAFE48ItjtsBAET+TmqkeoOpZESHTq2i0AGzyMi8iEXTCEvTaOU8WVGLrVb6ZVDTmsR5bQqCwGi5zddti81oZVuRtnlMImGHwyvJZfKJdingSANBEMLvX0GjPXu27TbXMt/DF9qmosro26x1bH2+izCtCZ7HVl3uLHnbsTXXQlZ/5B2vZUShZ30PZ8mKTjfNKgKy+yRtWzs7KfUfEjwDJ6qQ+D53lJIL7sB1l+Dvnkst9uJxVjKiQ4cDOFHFhEEvfVKT9jr87uZsTXRVuMkldiI6PDiAE1VM6O9MF2tJfm61T8HvbUKiOLht/GAqGdFhxAGcqGL8/qVrZ9aTaWPN5kkAERCFySA+8YADp8GCLkSHCQdwoooZDM7NRpor4DZWAABROEx5XuE1mUpGdJjULgo9TVZkYl4Upek6plGReRG1RaLH02RFceZtp4xa0LaiV7OUEQFvs5616bHW7z6f3PfGVE101+0gDEOEgy24jc5UMJsA8Nob2N16Yl9tMo3Atcnm8WH6fph+voH512HP23fW+2Qr2yJvO6bvk+n3Wt7rNt2WzTkKbEWn57XJpD583vvAM3CiigmD7fGX28R3nCMuRFwM+xfGxV6SByLAYzEXokOFAzhRxaiGgIZ7F8bR5o0VDLubSaEXjP8FU8mIDhsO4EQVFAbdcRBbcjYuImi3T2HQOzs9oUnyr+u2F9JWIloMDuBEFeQPr6TkggPN1jqC4dZsLjgQzyOO+cYfEFF1cAAnqqDh4PLMnOBQoJWkkmkU7MkFVwgEnndsQS0morJxACeqoEH/7Pj+9uheN4BG4wgAQP1eygDPVDKiw+RApJEVkRWabzp5QJH0BdMJRbL2kZWGkZWicL3nTOS9blupPUVSfrJeX1Z7TfddJEUvS96+u73N1Occp40wDOEPt+F6RzCeERzxGXr7FLa3vnLdfRdJvTTdVhabE3TYSvHKew2mr8/mBEA23yeT7QPmnyXT110k1dbmpDNZryOrXVnpYlnmnU4L8AycqJLiVLLZLxLHcSHSwGCwGVdgi0ZpZPH84c3mifIbS0QLwQGcqKKiKOWKjCo87zgGvfNJCVVc+xcAmg3eAyc6LDiAE1VUNEol26PTuQH93tlxJDowLrfKVDKiQ4MDOFFFDYeX4x8m8sEFQLu1gSDYHQ/g1wq6KAQORA5taAvRocIBnKiiBoMLs7ngAJrNNQARNAxmi70wlYzo0KjVf9VFJDWyzzSSGMguWG8aCZu1nbx9Z0Uzmhb9NymIb1uRiFrTvi0SZZ/FdB3TCNU8RSOld3a+jrUTd07XZlHAc5cRRRGCoIumMztYt5ob6CVR7KZRzKbR+nn7MJ14okgU+iInM7E1wVFe39rMkjBlOpGK6b6LRP7byvTI24et969INpDp6+MZOFFFDYYXZyuuqcIRDwAw9K+mrtdunyqtjUS0OBzAiSoqDHvQKEq9TO44bfR751LXazGVjOhQ4ABOVGFR0J8ewJMz8k5zA73+2dkodVV4jZXFNJaISsUBnKjCAn8HMooyV0CiOO+70zqFXj85A99zhu66rYW1l4jKs+8BXERcEXlYRP4h+f3lIvKgiHxBRB4SkVemrHN78vzosSUib06e+0MReUxEHhGRj4jIcXsvi+hg6A8uxulhEa4N5KKKTnMdqsPUam0CB67bKb+xRFQqkyj0XwbwKICjye9vB/DbqvoJEbk7+f3Vkyuo6uMAXg7E/wEA8CyAjyRPfxrAfaoaiMjbANwH4Nev2+CUCO4yotBtRs6arlPFusBF2NxWFluR60WyFGxFtU4u3+2dxeqR2zE1c4kCDXcFQRAgCn24jdbUc/Fl9FX4/q7xMVIkUtp0WzZrhduso5/FVo1vm1Hopn9f5DvS1vdLke2Yvu4ifTvv49PWsZa3nX2NJCJyBsAPAnjf5HYxHsyPAXjuOpt5DYCvqepTSaP+UVVHtSIfBHBmP20hOkyu3eeerLgGoOkuAwACfytlXnBguXNjyS0lorLt9wz8nQB+DcBkdMybAXxKRN6B+D8C33adbbwewIcynvtZAH+9z7YQHRoD/+pEoFpyNqEKR1wAgl7/Atqtk9MrKdD29iwjogPnumfgInIPgE1V/dyep34JwFtU9UUA3gLgz3K20QRwL4APpzz3VgABgA9krPum5B77Q2VcgiWqkkj98X3uPZHoDWcJ/f7m1Nn36Oc2U8mIDrz9nIF/O4B7k/vcbQBHReQvAbwO8X1xIB6Y35exPgC8FsDnVXUqcVVEfgbAPQBeoxkX+lX1vQDeCwDNZtPeTV+imgjDPjxZml4oguXWKez2noNEChUZX0JXRcNhEBvRQXfdM3BVvU9Vz6jqzYgvg/+zqv4U4nve35X82V0AvpqzmZ/AnsvnIvIDiIPW7lXVboG2Ex0KqRXXVNHx1jH0r1yLTB89oIDDCU2IDrwX8in/eQDvknjqoz6ANwGAiNwI4H2qenfy+xKA7wXwC3vWfw+AFoBPJ5GCD6rqL+bt0HEctFp2clyz6pFnRR+bKhK1a6pITfAita7TFKljnMVmX5nu22a9ddNI/v2+7sHwMpbbpwEZ3wOHAh3vJIZ+DxqFEGf6uBUIoC2E4e7+X0BOm4q8R6ZR+bnRtobHbZHskCy2os2DIGV+d+R/55geh6bvX5F+KqMOu6kix1QZWQTzZjSAq+r9AO5Pfv5XAK9I+ZvnANw98XsXwFrK391m1lSiw2l3cBYnwm8YT0mWnHE3G3ESSBD04XlL0wM8gE5rHdtdswGciOqDldiIKq47TKYVVYVEyWVyAF5yn9sPd+I/nJgzXAAcad+wkPYSUTk4gBNV3MC/lHop0HHiWcl6g/Px4I6JmUdV0WnOXPgiogOEAzhRxSkiaBROT1ySDNhNdwXdjJrobZfViYkOMg7gRDUQRoP4h4kBGgCWmhvY6Z0Fwmi8XBWIFJ67lLIlIjooapVrIiJot9szy23W2jWteV5GFHOWrNeQF9VqWv83S97f24qctclWnfIi+zCV1h/DcAteY3ZAXm5t4GrvqTgHPAJGyeCiCgcuolCheOHRwUXeI5uZDVnvh+nn0mZN96zjPGt5kWNqXpkN+2mTrTrzRT5jtjIY8vZh2rem719WphOQ/R2dlamQhWfgRDXQ8y+nLu80NuJqbVEwnQuOOJWMc4MTHVwcwIlqoDs8NzUhGYCk5vkxAIAfdPeUWo1zxVeap8tvLBGVggM4UQ10/XPjy+SjhwINJy5sNBxuTQ/wCiBSHGlxACc6qDiAE9XAILiSep9PkpKpu4NzQKRTgzsALHvr5TWSiErFAZyoJnr+pZSlEQQOdgZnUy6hK5ou74ETHVQcwIlqohucn1nmNDwcW7oFO8Nz42ptowfGl9iJ6OCpVRqZ4zhYWppNpTFN57jePmywmRpl2qYiKV6m6Qs2U0BM01UWmXZW5HWbpqVkbcdxPMAdTxsaj9iC40s34+L2V6EaQWT6WBFxINpAMMojn4NFTuZgOslJkWPKNFU067NUJM0wax/Zx4hZWl0RttIDi6QN2kx3Nf1cZr0XnuelLi8ySU3aOnn9zTNwopoY+FfjCUuc5CECqKLdOAFAEUbD2Wptqmg3VhfWZiKaHw7gRDXRHZybrsQ2KpmazEo2DHbHyycG8qOtm0ptJxGVgwM4UU3sDJ6HRtODN1ThIJnUxL80G8gGYNk7uYjmEtGccQAnqokg6gJhCIRxrXOEyUPje3Y7w83U9ZaaHMCJDiIO4EQ1crH7+LXJSkZn2Fv9ZwAA2/2zqdXamtIpuZVEVIbaRaE3m82Z5VlRn3lRgFlMC/UXmaAgi61tLTIqGLAXOVskYtjWRAtFImdNsyFM+wkAnrz4AE50boMr44/u0dZN8Jxl7A4vxAO4Io5QTzbjOs2ZfdmcaMSUzYlRTNtlK8sEsJf9ktcm02PEdN8231ebbGWy2GT6PZXXt7ZeB8/AiWrkWPvM1OANAK7j4YajdyDUASJNvsAnvh8EAs/hWTjRQcMBnKhGGs7sdLoAcKz9IgBAGPRnUsmgik7jRBnNI6IScQAnqpH+8PL0AA3EJVOduMDRMNydikIXxFfTjzOVjOjA4QBOVCM7/jloFE3ngqvClSSVLLg8VVL1WioZI9GJDhwO4EQ1EmmAMBwmM49F1+b9HvjbAIBh0sKqAAAKnklEQVSufzF1vQ6rsREdOLWKQlfV1Og9m5GlWWzW5c6KNs96HTYj3W0pUmM4K1sg63WbRn3mrZPFtOZyXpSvaW1l05rLo9f99NX/wK2r3zkVqLY7OI8oinCl/wxefGx23Za7PPWasvZd5Hi2FclsMzrdVtR6HtP31XQ7RdisFW5rW2VkMGRlHNkcGxad3ZOGZ+BENXO5938TZ9/xmfj60kuw0jyFbnB5XK1tRBVuvf6vTkT7wAGcqGZW2y+enjo0WX6ifQuCqB9XZhsVeoniS+wCgSuzNRSIqL44gBPVjB92U5cPo3gykyDsT1drSx4r3qkym0lEc8YBnKhmLvaeQNe/NLVsEGxjc/fx5Oed1FzwFW+9zGYS0ZzxxhhRzSgiPHL+73Fm5Q4c8U6h61/E17c/j1CHAOJUsiPe2sx6PAMnOlhqN4CnRegWiXI0rW1epK66qTKizU0jvrPY7PPhcJi6PCvqM6+t885IyHuPbEWbXy8KPf6bXTxx6d9wsvMSrHjrWHFP4fxwG4oIVwfPY71z20zBl7Z79Lq1sov0eRab9axt1Y62GUlsmqlgM8vEVsR3Gd+dRdpq+j1V5Pg0zWDI2kejYT6M2vqur90ATkSxb167B6utF137/fLgpXjkwkex7Z+LB+89X0Qt50jZTSSiOeI9cKIaWmvdPDV4A8Bq6wzW2rdix78Qn93sqdbWEP5/negg4QBOVEPLKfe4AeB053aE6kM1igu9JGlkcdEXzkpGdJBwACeqoTDyU5d7yWxlQTSYeU4AHPNOz7NZRFQiDuBENXR58HRcjW1SFGHXvwAA6PlXp3LA40eElcbGAlpLRPNQq5tiqgrfnz3zMI2KLLpvW39vq2Z2VSNIs5hG4WbVTs/bt2lkqc2oZFvv637ei63gAs53n8B655Y4WE0VYeTj6e0vIgxD7PoXcKy5MQ5kS9Y96q0jiqLMPiwj2txWRHmRfWTJ23fWc7Y+l0XqkdtaXoSt74oibSqjxr0tRaLT0+TO/WBlD0RUui9d+iSe2n4YQ38XQTDA1d5zEI0/7FcGZ+M/mgxmA9BxU2Y6IaJa4gBOVFMiDk57t6IZeWhEDk54N+Jbjr8OTelgy9+cyQOHKprCIDaig4IDOFFNbTRvRctZmlrmOS3c1HkpdsNL8axkU/fAAdep1V0zIsrBAZyoppoZKWFrXpwfHmowTiEbnYyrYtldLaV9RDRfHMCJaqobXk1d3nbjimt+2Jud1ATAUaaSER0IHMCJaurqqGTqHp7EueDd8Eq8YE8g2zGPqWREB0GtbohFUYTBYLZAhWk6Tp6sSUuyUppsFrLPmgQha9/Xm5gijWlKR5E+zEp7yOpb0zZl9Ufec1mvw2Z6ma0Uvf0ez33sQDWCwJlKFxMFEDnYGp7H2p5yq6NL6DbTBstI5TJdZ5GpSzY/Y7ZS7oq8btPPvukxVWTyGpuparbS/Yr0bbPZTF2elsbJNDKiA0ihCKMgPruOovihChHBknMMV4fPT599J/923KMLbDUR2cIBnKjGBtHu7EJVHPc2sBWcn1o20pD0//0TUb1wACeqsW5wJfUse8U9iX64nXp5z4EDgb0KdES0GBzAiWpsJ7g8/mVisF7xTgAAwmg4UxNdRHDEPVF2U4nIMg7gRDV2JS0SXRUtWQYADMPe7EqqONJYL6F1RDRPtYpCV1X0erNfSEWiO7MmbchanhVtnrXvrIjrvHbZirDMi063NcFEbmSk4aQlptHpRRTpK1Pzfl/Tll/1z4+enFruOe348xJsYalxdBylnlj1NvBc/9EX3KYi62QdHzbfb9PIcZsTH5lOflJkIhVbUehlfMZsRv6Xse8y2Mqc4hk4UY31o53M+9wOXGz556cvoSeR6kvu8QW0lohs4gBOVGsal0zdQwB0nCPjSPQ9xVyWGkwlI6o7DuBENedH/dmFCqx6p3HFfx6anHXHy+OBfFStjYjqiwM4Uc3tBleAyZnHolEq2ToGUXf8hxNn4KNL7ERUXxzAiWpuO7gY/zA56xiAo401KCJEWZfY3WOltI+I5kOqGqWXRkTOA3hq0e0o4CSAC4tuRE2wr/aH/bR/7Kv9YT/tT9n99GJVTc37rNUAXlci8pCq3rnodtQB+2p/2E/7x77aH/bT/lSpn3gJnYiIqIY4gBMREdUQB/ByvHfRDagR9tX+sJ/2j321P+yn/alMP/EeOBERUQ3xDJyIiKiGOIAbEpEfF5Evi0gkIndOLP9eEfmciPx38u9dE8/9voh8XUR2crbrichfJOs/KiL3TTx3v4g8LiJfSB4b83uFdiyinyb+5mMi8iX7r2o+FnRMfVJEvpjs909FpPJVXcruJxFZEpGPi8hjyX7/YL6v0I4FHU/XXb+KFtRXr0iW/6+IvFvEcOaWSarKh8EDwDcCuB3A/QDunFh+B4Abk5+/CcCzE8+9CsANAHZytvsGAH+V/LwE4EkANye/T+2rDo9F9FOy7EcBfBDAlxbdB1XuKwBHk38FwN8BeP2i+6Fq/ZT8/N3J8iaABwC8dtH9ULV+2u/6VXwsqK/+E8C3Jp+9T7yQY6pW04lWgao+CsxOd6eqD0/8+mUAbRFpqepAVR9MW2fvpgEsi0gDQAfAEMCWxaaXahH9JCJHAPwKgDcB+Bs7r2T+FtFXqjo6thqIB6fKB8OU3U+q2gXwL8k+hiLyeQBnLL2cuVnQ8bSf9Sun7L4SkRsQ/+f535NtvB/ADyMeyI3xEvp8/BiAh1V1YLDO3wLYBXAWwNMA3qGqlyae/3OJL5//5gu65FIttvvpdwH8EYBuxrp1Zv2YEpFPAdgEsJ387UEwj88eROQ4gNcB+CdbDV2wufTTAWWzr24C8MzE3z2TLCuEZ+ApROQzAE6nPPVWVf3oddZ9GYC3Afg+w92+EkAI4EYAqwAeEJHPqOoTAH5SVZ8VkRXElzt/GsD7DbdvXZX6CcBRALep6ltE5GbDbc5dlfoqOaagqt8vIm0AHwBwF4BPG27fuir2U3IW9SEA7x4tW7Qq9lNVVamvEF8236vw1S8O4ClU9XuKrCciZwB8BMAbVfVrhqu/AcAnVdUHsCkinwVwJ4AnVPXZpF3bIvJBxAfHwgfwivXTGoBXiMiTiI/rDRG5X1VfXaSNtlWsr6594apqX0Q+BuCHUIEBvKL99F4AX1XVdxZp2zxUtJ8qqWJ99QCmb8OcAfBckfYBvIRuTXKJ7eMA7lPVzxbYxNMA7pLYMuJAicdEpCEiJ5N9eADuAVCbCOu95tVPqvonqnqjqt4M4DsAfKUqg3dRczymjiT34kZnl3cDeMxWu8s2r35Ktv17AI4BeLOt9i7KPPvpoJnj99RZANsi8qrkVugbAeReBchVNPrtsD4A/Aji+xYDAOcAfCpZ/huI73l8YeKxkTz39mSdKPn3t5Ll9wL4neTnIwA+jDhg4n8A/GqyfBnA5wA8kjz3LgDuovuhav20Z983o15R6GUfU6cA/NfEMfXHABqL7ocK9tMZxJc3H53Y7s8tuh+q1k9561f9saC+uhPxSdjXALwHSUG1Ig9WYiMiIqohXkInIiKqIQ7gRERENcQBnIiIqIY4gBMREdUQB3AiIqIa4gBORERUQxzAiYiIaogDOBERUQ39P3b1ZSKe7wZqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "srtm_wa_subset_vrt = rasterio.open('/home/jovyan/data/srtm_elevation/SRTM3/cache/srtm_wa_subset.vrt')\n",
    "f,ax = plt.subplots(figsize=(8,10))\n",
    "for Di in D6:\n",
    "    ax.scatter(Di['longitude'], Di['latitude'], c=Di['h_li'], \n",
    "                #vmin=0, vmax=2000, \n",
    "                linewidth=0)\n",
    "show(srtm_wa_subset_vrt, ax=ax,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin,ymin,xmax,ymax = srtm_wa_subset_vrt.bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point, Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def point_covert(row):\n",
    "    geom = Point(row['longitude'],row['latitude'])\n",
    "    return geom\n",
    "\n",
    "def ATL06_2_gdf(ATL06_fn,dataset_dict):\n",
    "    \"\"\"\n",
    "    function to convert ATL06 hdf5 to geopandas dataframe, containing columns as passed in dataset dict\n",
    "    Used Ben's ATL06_to_dict function\n",
    "    \"\"\"\n",
    "    if ('latitude' in dataset_dict['land_ice_segments']) != True:\n",
    "        dataset_dict['land_ice_segments'].append('latitude')\n",
    "    if ('longitude' in dataset_dict['land_ice_segments']) != True:\n",
    "        dataset_dict['land_ice_segments'].append('longitude')\n",
    "    #use Ben's Scripts to convert to dict\n",
    "    data_dict = ATL06_to_dict(ATL06_fn,dataset_dict)\n",
    "    #this will give us 6 tracks\n",
    "    i = 0\n",
    "    for track in data_dict:\n",
    "        #1 track\n",
    "        #convert to datafrmae\n",
    "        df = pd.DataFrame(track)\n",
    "        df['p_b'] = str(track['pair'][0])+'_'+str(track['beam'][0])\n",
    "        df['geometry'] = df.apply(point_covert,axis=1)\n",
    "        if i==0:\n",
    "            df_final = df.copy()\n",
    "        else:\n",
    "            df_final = df_final.append(df)\n",
    "        i = i+1\n",
    "    gdf_final = gpd.GeoDataFrame(df_final,geometry='geometry',crs={'init':'epsg:4326'})\n",
    "    return gdf_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_0 = ATL06_2_gdf(files[0], dataset_dict)\n",
    "df_1 = ATL06_2_gdf(files[1], dataset_dict)\n",
    "df_2 = ATL06_2_gdf(files[2], dataset_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4eec21f438>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(8,10))\n",
    "df_0.plot(ax=ax)\n",
    "show(srtm_wa_subset_vrt, ax=ax, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "srtm_vrt_file = '/home/jovyan/data/srtm_elevation/SRTM3/cache/srtm_wa_subset.vrt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 1 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-69-bd08bd5cb426>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mrefdem_sample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrefdem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoints_xy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mrefdem_sample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/lib/python3.6/site-packages/rasterio/sample.py\u001b[0m in \u001b[0;36msample_gen\u001b[0;34m(dataset, xy, indexes)\u001b[0m\n\u001b[1;32m     21\u001b[0m             \u001b[0mwindow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWindow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol_off\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow_off\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 1 with size 0"
     ]
    }
   ],
   "source": [
    "refdem = rasterio.open(srtm_vrt_file)\n",
    "points_xy = list(zip(df_0.geometry.x, df_0.geometry.y))\n",
    "\n",
    "refdem_sample = []\n",
    "for val in refdem.sample(points_xy):\n",
    "    print(val[0])\n",
    "    refdem_sample.append(val[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 174 beam/pair combinations\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f8d233f6b00>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.6/site-packages/matplotlib/colors.py:885: UserWarning: Warning: converting a masked element to nan.\n",
      "  dtype = np.min_scalar_type(value)\n",
      "/srv/conda/lib/python3.6/site-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n",
      "  data = np.array(a, copy=False, subok=subok)\n"
     ]
    },
    {
     "data": {
      "image/png": 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f/a0YaZfsU1XkakBlwb8DjCkoLExRcHXMdj+3fn8p51y/i4Q3wCvfeNkMK8q62FjTCkMgLYUI2tjpUt/nYNY+vJHV9+3naPlMim0fqce4x6JiY4bE8wrXEdhmhI9+51oAjAdyvPyXixi4Yjf+u9QhyqUQ4KmxuPflk5j1k0HkqZCY7acsbxMZ7qA81M77zl3JuVeuoP0H36E3UYmnNo89Ok7fTaeCZ/qENlT+cBuhjeNoH1BcM6ub769Zwt80rsK0dD619WJGMod6Mx9tFAJTHQ/T1NeWkhA4AVG6oPOL85j1b9vAcVBC8Ir5Tefn51G/HmRCw3HANgAFIg0yoYNQTHbrzG3rQ5XNZGemjAXVvcz2TeIowe5COYPZSsRUgeqGOIM9lVy0YBO/ueF09FMsrAGdgbcHoSNBLOPsFQCiphqhabiTk5BM4bTW0RyeZFehFg8SYUNsGwgXzBAUKgVGIYi7XqI8CjlXZywV4T11a3hhqhXT1VkYHqDBmOCR/BKEAaZPoBKC8MAbPxvUa03HS10H/O9GvWS/p3D+yoe7XIO5GoXfOVCo23tMqMPH2G5Y8jGX/lfp7GqGi722HHOhxB0fZ9E7cry8q524msWo1yA0rxYpJZcve5bfv3gG33z6Usb+OUJsk8D0Fkf2PAUYv34ebf+0mdsuepgaGeXmnQv48P/8LTJsUjtngtBNfnj3UWygw1B0Fnvjdyje+HdQ4k/iv1atBVV0zNn9jwspzAig/D4KVQF2f3Eh3qzE9AM+F5kw0MaKi0wYoIOQivTLdfz2N2dx2Wkv89SuuVxbuYaRXISRfJRrK9axbaoRsybIqS27sMsF5fPT/P1P7+eKU1/img8+C0EN5pdjVQYYf9tiANTwCO7AIGRz4DUYW1BHT1clJ5+zC2GAHZRkayWZeokVlSAEwZyi6/YaCk/qtJ03wuqds+hOVPDeujV8tOVpqvQUt+w8m2AOqp/PUf1sjrApMazSxPDBzFzaesD/nq4U+Zo68t/QSX7IJnO9i9Vfy6yV+yy33/ezx6i6w6Du8qHDjS6iFNgZmLHRwayPYewZZ+POWbhKAwTZgsVHbr6HbMHE8fl498pnyeyJ4vbk8L2rn9zcAvmlOSLv78SzAbqWhrFdOPejHyDcnyDQBZEnJf6PSh554ktHuYUOz5vBRLSkCZxgfOeup4nYBRzHQYb89F/XhmsIhA3RLkViDgS35xnzSlxXInISgUB5XFTMZN2aWSxd3MeezTUMJiStlWN8+PGPIsM5dF3xyMvLwJtEW9lAKpvk1BU7eCHVygWx7cxcNlKsxFMKynUKjRmy9QqPvpxwXxKZs7DDBunaMKkVNgQMgs94iIZSjOdjB1j+CEcRapzCLHjxrsrRe3UZ9ckC9+1ayv19i/BIh4LlQUzpnOEfZHtrNcJSGL02K2tKj/3BnHLREk6/YvneISHhwklel50rqpDj1ShNEZyn82/v3Re95W8WrCd/y5nc9fAK3tr6HKvUmYcMCYWfiTB5io02qWMumcHh/EC/dtcTPPV8HRf8zSAK0CKSgbif0Mw0rgNdndWUXTzK1O4oPs1DXWiIzK0Gyh6BXB5lGETLQkexdQ6PUgJHvTb9aCFEI/AroIZihIVblFLfF0KUA3cALRQDbr5LqWLsFSHEF4C/Bhzgk0qpR6a3LwP+C/BTNK75lPoDQeJKb8MJxMY9fShdUr4mT7LNw+TJ4BsH7yg4PsXUfJA5CF88AekaRIWJCgtcVyA9LsqWOK7GV5c9xPMzdL53+0e46q/u5qQZUzw/1oajdObP7mV22QCrnzyNjrE6ZtYOszNXS3e+inmBQUDQvOxlejYsxtcRwTOcY/QMRTxZjjctKIQUdsQmvN2L99IEe9bXIs+fIvysn3TEQEmBNBXVToJsvUDoCj3kwf9dxXlX7OS2nuUUynQKOuh5CEwq3lKxh77eKDomjz/+RYQ89h+M1ztSSm6857NF66ANXcw8uZVTLlpCys7z6J5tVIZCvKVhNpo48KP3iYXPsSTwcXZYcZ7+gY59jo0mwS5A7N4wjl9hpHSyzQojXbZXBHiHMgQ7EghbsXMSChVVvPjJDJmL9xBcEGDxjCEyeR1NWoyGKujrD+PNBfhez0x2lTVTl+4serUH/DAeP/YNNo372vXybeAzSqmXhBBhYL0Q4jHgWuBxpdRNQojPA58HPieEmEfRf2o+UAf8rxBitlLKAX4CXAe8QFEIXAQ89GoXLgmBE4iJ3qI9vlPhIdxnkkoFyM+A/IxXOgkCfUzQrVeALXHzOkgFElxTB1sgFJT58gg3RnJhlt8OLGdx5SCfmvcEoJM3JT/rXMHiCwfY9XAT6eopVAzihHg2ORuAng4vom2EtF7NzLv7cMRMCmdmMBdaiIRG5MUQwa40bc1DGCs99PZUUvH2QVp3CgojXgJzMww1G8i0xuRsge95RWDMYXCPh++/5wG+/cRbmMgGaYgluPGdj/CrO2ez6skbj0ubv5GQUnLqJUs59ZKle7dFjQDvnPuHg1+ubP8xK4EfZ7+N+6jYqw1Y0eJkvJFVMCEwNQ9ewNefpvz5fc5dmcc6qJwdYmzFLPBP4hNZtvTHaCi3GC+EGImHWdg8wBZRw4NDpxFcN21BlsujMllc2z6kTseC4sTwa/MJVUoNAUPT6ykhxHaK8dKuBN46fdgvKZraf256++1KqQLQJYToAFZMR2iIKKVWAwghfgW8nZIQKAFw3jnzkXc9iJYXKK+GMSEQDjhBwAFPUqIXQJUV0Lp9uGFZFAKC4iyYLYg6RdObKX8dd172M24ZXsDWwTbWjs9GCnCVpN43iGoTrHz7Dh7rWEhoRoaKhuTecETl/2ER/bFFrzeONTZB+eYwOVGF7QkglCLQl8bfPc7yaA+dSyuIrfVheRw6ZkXQ5rhYKkKLZ5KO9Y3kF2jYzw9StsBh66JK2u9NcfunbiXgcTBdwb2bm9h4RtXxavITClcDKwS2X4AEzQYZB29GkStX1KyNk4nNILz10J57rCMNgSiBtw3QubEVz2CIHiWwUVjlBXaoajRNJ7vBoNxjo7I5UArXcaBwaKTYY8GfODFcKYTYP879LdNxzw5BCNECnAy8CFRPCwiUUkOvRFygKCBe2K/YK0E2ren1g7e/KiUhcIJRfv8eJi9uonyrQgiBkRRwUEofZ6vEV5lHTQVwAi5oCmELZFbinX5/1+aq6ejTGDerOatpNzuydYCBLjK0G3E6RltpnTXGWRO9vDBRT89QhPLmKXSvTeIcmHyhCTnlkKaLip5+/LaL0DWUq1DpNO7oOF5po1s2/kCSyVuaaD59Em9zHmvYIPF4DdG0i3UxuF/2UtE2yMC2RtS70/SRR7eK+vXCeR2076o7uBlKHAVyMYFTDWhFzdJRgAf0EWCygNE/id1WhZY5tOcuXJicBWM9QfR0GLtGYesKbPDkfNg7HJipkPPTsLkSN50+pvf2ajhH7gMwfiT5BIQQIeBu4HqlVFK8ilMnrx5k84iDb75CSQicYOQW1+EdzpGrCyFMUAdHBrDBM+xBOAqfBDOmoWTxJfWkFM6wh28/dy7rVCXWzqW0/nKI3l/mqNJMpG7i2jBoRlgcHODJNfNZtqyH+p3j7OhoY3BzMUOZ0QCNP+vEtW26L6yg4tEJnO7eomH5fvNXv/nATALvDtBijjI8pjN534E9ekPZRLNZfHUWfo/FgrYO3jpjdzG6437HXVz3B3MRlXiNKAoA9n2GBBCDQkrBiixXZJfym+EJlM+AdP6AskrXqFrYyfDWObg1NrGtCqQAXZE4SUefCGBnHTzROHJ4X69FeL30vm8x537zGzzx2RuO1a0W6/waewyLYraeu4FblVL3TG8eEULUTmsBtcDo9PZXC7LZP71+8PZXpSQETjAiL0+QqQ8weY6Na3vQ86AMilEaTXB1Rb7SoLwjhyemMJLgGiAtkHmFlnN58O55qHk+lDdN+pQIqa+0kbxOEA0VSBU0rEcDeGpHyHfNIL5wlPbmYbaMNKOnio41CkXPNa3ocZf2e/fs66YcZMCgzApikUleerqJwOHiSbvFiblZgVE+Ubmaj/deiNF2qA+A/3BRy0q89uwvAPbDKgNnIMyHvvZeOm68jc1Zm+AOG2EVNQKlScpbqtnhdRBVCn+PxtQiil+nLEQ7FImZAkyB6Tj4RzMMfug0XCEwQy6OI+ntPPZRRKE4/PlaIIpd/v8EtiulvrPfrvuADwI3Tf+9d7/ttwkhvkNxYngWsEYp5QghUkKI0ygOJ30A+OEfunbJT+AEQ9ouMmNT/7sket7FiikUoBywIgqhFK4fpOvBSFh4sgojofBkFJqpkIZN1foxxIQCQ8O6IId/yKTti3kqPiVoucGh7bEUk9MWODO0NNVGGrvKodBgYs6wKDRZyCkXZ1TRf9V+kUQND3j2RazUukfwDqSZe/EA0j401IPuyzJU4+HSsi4KjodPNj7OxsmGQ457bryNrlLCoeODALwuZS9pfHDljTzrjDOx2I+KhFDRMCoahmiEZDbPyZle5KDEvyTHdYs38tVTnuWcOT0k5gqiu2xAIG+txPZ7yIUEBR+AxCnL4xs99qFhiwHk5BEtR8CZwDUUQ+5vnF4uofjxv0AIsZtiut2bAKYDat4JbAMeBj4+bRkE8FGK2Rw7KIbqf9VJYSgJgROOAqAnbEbPrsD2S4xRkI4L0sU3qoommEicJkGuTOIdy+NJ2RiTJkYiT7rGy8pTXqKmy2b2t4cwvENkl1cztbSG1KIZpBZXk1pew6jfh9Qcdo9W0tlZQ7DbQRnghopRJrULxrAaHFTWwVk6F1qbwLLBtqClEefkeahEGjNscMaybiouGkGm8mA5iIKNNpVhxjvHMchzb3w2STvM0lqTH+4+m01TxXkwy5U8PjKHO7uW8+1bbz2+DX+iogAtS3auSSGeIV8XIDKSKyp1ulZcBNimy+C6JmYsyvCb8+5ha28V//PSAj7ctpEvLX2ORL2OQNBS8IDHwGrIYzXnMdvyeFJe0mcf+8lhhcBS2hEtf/RcSj2rlBJKqUVKqSXTy4NKqQml1HlKqVnTf+P7lfmaUqpdKTVHKfXQftvXKaUWTO/7xB/yEYDScNAJhz40jNlUQ/Ujw/S9qwE7si8HrDntjyWUQO8tkGkJMTnXj5F2cAwNx+dDZize9eUMq98+xugCH5kbFMHvxTFfqMIz5WDr4FYpAvMTiKyHHX21JIJT1D9gkTjJJVcrMCYVY40xmu/tpf/amWj/vR01rx33tAUgBSJTQNu4nZF3LMLpTJBzuml52wjUQO55H+gQOidDw0njeB+p4+mBKoZHVvKTpb/DjMNX111BLJLBVpJkwk+tO8XuxzNw/fFt+zc9UzbE9AOiuGLBHe/+Ne9b/x6EW9wYjObgMIljymqTLKyc5L2fvppQf/Gj/vl7LiB8ZZba2jT2/RpadxynoQxPnxc0CI25WBkXbVHi2N3nNErxmjmLHU9KQuAEw1zYjrF5DzQ14AqFdrB1g1uMLTQ1M0xgyxj5BdVYoeJjIl0IbB1Fm1TIVI7MwlZi3gwTUxpV53fhEMJAEdAm6OmcSTjtoeaJLkb+pop6HGLbivF/ALy/2kr+tAbKn0gx8q4lVN+58YCU9dZJLSQaBc3BPL9/YAVtE1uZ9a5BrNM8CBSGsun5tqBlQYGxAmwcaODxxpOYb02yw/ZTb6QQKAZMnQp/lnFRshA66izIsmxyjPXj7eABJhVy8RQ3dr2VXyy8h3/hLDTDIXxGivxzB+UJ9itmnD3Jmn9tJDKSRBQsQOHLeCj82kfdxydJ7faiysJgFnMSa3mYmu9AXZbQ83VFt6pjingtncWOGyUhcILh6einsHwmAh+Oz0XLSoQLShQtgJQExwBVLqneHEfYDvm5M0ApfNtGCW4a4bFNs9n9mWaq1ygmTg6BP8VIZyPS66IQqEI5MpoFPIQm8ni+nUNmLPAaxXF/pfAsnkvg6c2Y1WXE3xtFRVcQ7sihmYpcrUGi2uCv3n4nYSvMA/9yBrVfc9n5ZQNwkQZkunRO/kaW/l6XpbFhHhyo4EvrzqPl9ybvvmoD67QZ5G2dv5nxDHf/9HQKlcbxbvo3PY5ls6UyjKPykDGgEpzRICoHn4wAACAASURBVFvcJgZq1pNdGaWmMc6MshxjfyfI/zaA6teQLQ7G1RkaYylGhrKIVGZvqHNhOUjHpWNLjOr4MJkZYXyDw/jPTGIqHxHNIvlELXnt+OQYLmkCJd5wZEMaga5R7KZqXE8A26+QTtFpzDWK1kGOTyHyAqe9mvCaLsKruouFPTq0N3HvmIGYnWW8PEDVThdz4RBTnlrEK7nBBShnCleLEDjfxL7fBtdFZPKQKZoGDlxRRyMLKZg5Tp69na3lDgNVNUhH4gZN5izcxN0jc2iVEdQihwE3xhnfHmJizI+dF5Q1FHBsQVSleXDzfESXjZqnkygPc+8jp+KbzOE6kntidaSjPpT/jd9je73zX0v/k2t//yn0uIFn2iDL9hhYeY0fRk5HfsLhsood9OyKUb8wzuTcPKar4ZEOET1Hm5ljfTINQiCmPQuV4yDSWULr/aQqPASGEtheH+qJejwUOy+6RxF58fhM/L8ZksqUhMAJxoKbrqDzuruQ7R6McQuzTuJJF18mRDGGEICWV4yfHqLGbEekc+C6qHCQTLOXKV1DWTnsJcMUnq3l6wvu47sjl5JI+rCVRnk0z9mRl/nZUD3hah1z2yjmYCUqs6+3dvrFq3kucAbN39rMhntXsvTqtVyw5GlcpYNyeWhgJplVy0lvzTF1jYkrNZ4fa2NFeRcBrUBPtpLnxmcyLzzIhc3rWHXnAgptfpQPcj4vlm4gFSR8oAIC1zo+oQVOJL7Vfw7eYQ+6FKhpYx3DAm1Yo7eikpoqm4iZozagON03xRpVzqjtIywtLg/1cce6WUUBsN85BaCEwE24jF7dxMxfDzFeGcNVCtcLWkER6ikQOA4/r+L1nzDmSCgJgROMq5fO55Nf6KHpwSSVW0wGZngxI6po4+2CKIA0IdbhMnGqpO+SEOWbDbSCItnmId3qwR9Jg+nAaIz4SSZex8PXWx+gIxcmj5eZnnFaDMEvCoL28hECN0Xo+FuJKgB2MbDu04Mn8ZblG+i6cBlOo8OGR09lbeM8iNow5UEfDOEELQpenXSfj7J2E1Pp/HfPabhI/JrJ6ZWdVFiKXclKfvTz2/m7b1yLSCso92FVTqslJrgWhPeUsokdbfak6tB1gRsrQEsBTIHqDqJlBAx6GMuH+OV9LVx78jbunWhhxUlTXB7tZjAX5Nad80g+OoYgRrKljEJrBJlziK4fRrNsplYG8cfBSaYIVRr4n8yifBKZd8lGBYGt/X+8gq8xCrBeo9hBx5M3vi5T4k/i3PmzMOIurqMwUgLfmIPIg8wJZAFkAWasTuLJOQSqk9gBjZGVfgbPD5Bu8RQDgoULGNtjiJhFuD3JtT/8GGsnK3mLL8VlgUl0R+MTu88kZJvsHKthUaAPeyiL8hsQ9KOCfjybDVYlWjHHEhiDArvaIrYuTHRVGbH1IdyAjQzo1F89QLjTZaUepyqQ5srGjVxW9zKX1G/GUjqXV/XyXHcTvWP1xHablHdmYcLEdRSuAle4eAZNgvH8H2+cEn8WuWEvzoIk1qwMlgesoINaPIU7w0Rmkng0G9Pv4/bvNhDbOc5Ep8VPnpnN4+sqOdu7he6HGhk7v4X8/HJU0INT4SV+QTPZiB/NY+BNQXppDcGtA5ghjXx5gELUQ3BLP1REj8MdH1kugVI+gRKvKzy6RnDIZnSZjxkv5Wl6ME621ktyTgA941C+JYOWcVBXZ0kmAgQak5jDAVxTIgM2vqYM1oAXN6AI9JaTypnUDUi+85MP8s9L4mgRF3dPgEiHn8olKV7smMu7TnuBQnMUXfOjZS2Eq2CrTdt544w01RPZbREP+5ia7UDQhZxAuhrGANSeP8bAL8vYOFXOJ2bu5BcTs4k7XmrcPJ+v2s66XAWxNNy06jLUJVB5d5yqrjRmwguahrBtPJMmJZ/ho09gT4HMTANGw2BLQGGFLDwtGc73v8igcyoTF/thaCbP/WoSvxzllA+NEO/18oO/bWPyjArQBOHOLHrGxvVKUs0BMmfUEu12GZurMJdGCG8O4escgrxVnH+qnQF9Q8f8fhWvncfw8aQkBE5EPDqxzjyEDES2QGjQJDS433CJENg1BYLDQTJ1NuG2KZSSSOFSUBLjJZ3kChNPnw+EINEuKN+jKB8sK1oZqaINdZ0/RZ8b4eZvXordFELGNVx/cbDYWdJH/gd1ZBdrRJ5IEBr2YpZpkCwaisoUBEYcen9US/w8L2u21/D+Wbv5av3GvdVMOTq/nWgh0WoS3eBnqsal0KLj77bxxfc5D9lAYnbkmDTtiYy5XEduCOIREmUIcBQiLink4Zm2UzmtfBTWVJP3+RFVkvyUwaofJEEItPYyaC2jfHMK16fjBAyk6RDbkmRqQRjpgDHlYlV5wLJRmX2anRgaxZzV+gdqdvR4vffyj4SSEDgBcQwIFBRClzge0A+aVFO4jKoy2s8dZudjM7Fbc4iQg5vUcHv8lO9OMtFkkW2QkJVkWy18CQ++KVXsHkkwQ4LqwDCheDX53RHq5qUZj+9T2Zs3NtL9tgLKMBluylI34lDIKFyvRCqFllZ4Mw7dq12YA9mnKvnK0mVcEBtgnm+SASvEQ4l6UhNemht6Gd88n+p1NtmkZGpZDH3Axmu5OALydV582w8KlVriNcfaHkbzSPQMyKxCScgFBMaogZjlYdPaVk6qGiZPlHBjhrQKI6L7EsT7RvPYMS9a3kXPObi6wCr3E+zNYZf5kYMOzPSQPKmZYCKHli+gpMD2G+RqjkPYCCVKmsCRIITQgHXAgFLqMiHEEuA/AB/FTtrHlFJrDlOuG0hRTJ1mH0kY1hJHhlAKWyo8QDbsEIlLxHTGKOW6JNt8+H89QudXKgkunKSwLYrt6EhcKtZkkCkHChEIJvCMlNE0fxd9U/NJuxpaDmwfCBt2DsX4zBUP8YtHL+TcuV08MDSPTG6fvX79SYP4vFk6mIPnF1NolVHwKUBBTiF7xgkF/PjW6aiEIr/HxwOtjfyeJlAQkAWGHm4kd+4AU+cUyDzpRSioWZ8iVaWTafBCziG2PUk2WPITONpoeS9l/S6+rItwXJQm8Qck8UaJKggCrs7GXTOpCeSZdVUH3Q+2MrmrqKEFqnPkpzR05SKmjcg0UyEtF9srSM+ATJlBbM0Y/qxOus4PhEEI9ME4s7q2H/P7LU4M//GQEK93joUm8ClgO/CKPv4N4J+UUg9NB0j6Bvsy5xzMOUqp8aNfxROLwKiN6WbwyAi+Tf2MXTEPb8pFWIpsg4/wE3swJtLkuuvINTiIU9KIjCR6j8Q/ZiELJqotS8UjFUwss+l84iRYmEKlg+hZgeMD5bfICoMN+RnYDQ6Faptv3fAQ/+/uZXT2lzG7ZZy5sx7mxluvZu65fTBqoMayWOVBhAv6VAahFFPVfjS7GGK679EWgvVpKhbEyScMBte1YaEx0VXPF858gn9rvBDvhMFkm05orIC+O4/SJOnKAGath507dzJnzpzj3fxvWvz94M275MokCA1hK3xpl0gfTDZEmFfTzUazleQ7TJyY4Kx/2USiO4hjSspmprjnr89F2Ac6fQk17cRoaLxlxQbC2TS7nm3EN75Ps1NAPHs8hPxrl2P4eHJUhYAQogG4FPga8OnpzYp9AiHKH4l1XeK1J1obIbktCy/vRORtyu54GasxiuvTKX96Cmk6JN8Sw5QaoSd0pK0I7DLxDuUQShWdxjrGmTjXh8h48fUlyc9PsWxpL+m8D59eIJlTjNw/l9UxP8FrJli3tYraeWm+/g+P7K3HL0YjqKEYu3rzNIeDeNNZjInpZCFC4AQDZBsi+EMORhrsSofMYIhsXwhEMZOVW2NTO2uQ8xq38++FCyiU2RgZnXSlvxgtD8ADtoK33Xcb3Z/9p2Pf4CcIgayD49XwD5p4LDC9UKgyCCYdpvIa9bPHaW4f4qGheezM1FPvpoi2ZPaWF6YN8tCedXE7/MXJm9g8VMOuw1y7deVxiB0EJT+BI+B7wA1AeL9t1wOPCCG+RdFE9YxXKauAR4UQCvjpq6ViK/Gn87PvXsM7P/AfaC/lcXyg5cDoO/Al0nMuvq5JXCTBB4YwMn5EuBgeWqUzzPpenI6b6/DEFfn5krrfV7N9Rx25GlVMVj+sEe3KM3xGhLneDImlkoen6nhosol5gUm68mEe2tLCFX/1GKtuvpJ0q4Pbr2FYxak2W0rydWESpzhEMhPkzWrmnL2bHY/MRs8XXzw7oJhzdidbd9SQXBBm8amb2Lx2KYUYGFmBnB4mdr1g+hQES/MCRxcHz5iFTNkIVyEiOqFek1SDjpYCS4PGqgTBf08yfGUVz8ctmgMT+KTNuBlCJLKosvABU60KkPEMOMV+Y8+zlYdeVinc2MJjcocHU/IY/gMIIS4DRpVS64UQb91v10eBv1dK3S2EeBfFRArnH+YUZyqlBqdzaj4mhNihlFp1mOtcB1wH0NTU9Jrfx5uRGWUR7r/jk3z954+y5h/vxPIWkHZR7S5vq+L2nT9CSskF8p17yyjyOPHJvf/vuXkhKu9S94su+v9uJqHOMTzZCFanVsxClixALgUiQjLlJ+TPM5wOsrS2l+2FGBFPmrcteJH1E81Uv/tldm6aR9VLMcxCcUzY0QWJAJyydA35ZxsZXZKjXY4TvCZH5+46hFC0zx6iiTibJqv4wDPvYm7TFvTZ4xQGylA5D3LaLtT1gOWzMSqOdUufWHiHLQLxzN7PopECSwr0UBTXMAgGTcatIL7VedQpNjuMRuL1Ibxei0zaR2oBlG/PoMIBhBAopRDJDKNL/YQ2DLG1s4aeZyqKQW9FMfG1chW2ENz8q28c8/steQz/cc4Erpge9/cBESHEr4HLKc4TANxFMfnBISilBqf/jgohfgusAA4RAtMawi0Ay5cvP/ZRpN6ghAM+/vWTV8Anr3jVYx5z7zrsdsdxmHnHvzHz5ixyeIzmO6OorR348q14g95iTy6dw+nqQZhLSE4F8asCK2v3MCc6wmIGAPjczVdTftp6PjF/JzvKdvLL4FWQBWGB8itkJMH7m9by3Z6FtHysl9X/soC3fGYjDUsncZD40zZP//RkyoKK0YU6a7qXcOXitTwTnEl8oBoK00MLXhejfAxzSzW8+zVuyBJ7eUUAvJIXVwCGqzBHLdwghCd11stqVFCgNiZJh5tJTQX3lhdnKALxYfy9iWJIcVeRqtYoWztG2fl5Xs41MD6/nIptk8VhScDyaiRml/PC4+s47bxjbzvyJySaf91y1ISAUuoLwBcApjWBf1BKvV8IsR04G3gKOBfYfXBZIUQQkEqp1PT6hcA/H626lvjT0DQNKUFu2QOGjrapAwB3TxewL6u1AOSwQdLvIRQI0BKeOPA8ZoGpNSu4paKOcmcHnz/vNh4ZWUzO9dHgG+MDVav5dOfFLDp5FJ83S1LZPP71U1A1GpqusOIS31AO79kWZtpgfks39248CyYtyuaOENDAdovmZeNjAcqbSsNBR5P9BcD+eKcyQBlbNjRw7hUdPHZ1lML6IP7GJHZvEMeVaNJFb8qQOStG/y7BQl8HhUcdooOC2N+Db2Wx5185kSY+qwq3xgeOi78vS+XuJDf+5S08PHZshYBSxcRFb3SOh5/Ah4HvCyF0IM/0UI4Qog74uVLqEqAa+O30A6UDtymlHj4OdS3xKmieYqjfTGsTwZ2dqLpK5GgCZduAQugepM+LJyPBEQz2VRIvD1PbuG9I6bL3Psd9t55H96CHrp4z4cyXuaJ6I+3eNGuy1fz70DmMb2vhi+f+ji+MnI5cZOJ92oB4AQV4pARdYzKto4eSbN2mEakax68ko2vrmKw3QVMwpeNN2qQib3zV/Y2GEAKhFHrS4OYPP4BXSH40+70sMXaQ7ZpHYX5677HauMSeUAQNCef78L9DY2BHNUnNos3sg5wgN2VTPhGHAU+xt2Fa5AICr31obumjTXE4qCQEjgil1FMUe/4opZ4Flh3mmEHgkun1TmDxsahbif8bwgXRWAczPGA1MXh5DXWPT6J3F933VUWMySXV+DMOttLRNMGqVYt4x7ufQdOLAuTxmy/gi5+5ja89fBUy6WX9rkVsHGojHFCkshpOOkDoOS+f2/1W4tEyZK9CeCWaLEaaVIDj1wnvEZipMqyzobUuzqYXmjh9/i42ZRoBqG8cwkn76cwHjlNrnbgopRhtBdtnkywYVHhtop0e1rctoHrYJPd7P3adg0xIgttTiKe2IkzI3AZWlY/MB1tAC/LS/VFqe7bhTxbI1cbAq4MUkJUEhqewj9NAcMljuMQJi0gKVP8wmtdLZnYlhdOSDMkK/PFypAX5MORaLJbHJ9garyavwcSL5dzRcyGzl/cgpSI/K8s3/+sqohekSe6I411fQ2jMi4pJYjkXx4T8fJPEfxdIfUQSWQ8qoFPMUqj2psUUjoOWAl4sY5OEcP0gqwfbMVxF2Ftg91g9+G3+om7d8WyyNz3ZJgj0qr1DQkoplIDEJRUIR+PO0ZNYvGuERYu7eOGpucx7/04KY16mdsfwthUY/uEwxR93ej5hLE/Z7VuZfN9CQDDaPJs69hAYKc4ZFO2E3WIY9GzumN9vyUS0xAmNZ1sAGQnj29NDdlYFzkSA/Iop8soLjkB4XMBkYWsP3b8NotkhpKmYGouw5qGiOZ9zyQSOXkVboI+EJ0bo7GEmtzUUvUdSEr/KYJo6alYjM5d107OmEX9u+uP/ytizUtjS4bQrt7Dx3pNxno/hrw5hLxon70aYcCWyPE+9HGLjtiVw9XFrsjc9hfd7yD0aJrZhEukqEi1+xs4MItIST17wnYfns+ipZi7/5AYem30S/vI8wcoc5XOnmHgScA/9oPr6U3vXHaXjJlLIaBjUPm3QtR2wj0eIwDfHcNAb/w5KHBfMWTmQAq28jEKZgCmJkwjg2A7oNnbexR4L8Nt7WvjoO59D6SCsA19UdzzFrKXbiXmSiKcMss/WQCBLqH0YWuPkx0PoixO4OuheE/dME5EpgDsdoc5ViKxFYrbOw0MLEYuzLIt0YW+0aOv1c9f8R3hy5V3cM/9BqgdijHV7j1NrnRhMjHkJXD7FnvdX0/XOauJnRNBcDS0rcHwCw0jRflU5e56t4MNnP0Yq5dtbNvX8EVzAVSjbwk2kcC0bx3ZwUylUPo80jk9YEHc6z/AfW17PlDSBEv8nVIWLsiyEIbAjOqKrgDAN3JAHRwMc0NICNW7y8xtaSVxv4ykIIrvy2BEvSEHrc03sijgoCpS9r4v0w/MJzEmRG4viDZrkl48Q+mU5XTLHfJWjYf4wu4bbmPFiFj0PrlTEF/iQzS6nV+1kOFHP/KV9bB9tZHCLjw8OXg1+wAJPyqEtOXK8m+1Nzf9n773D5CjOxP9PdfdMT97ZnJNWOSOEAlkEiWCSMWcwOJ3PZ599Thhn/87ZdzY+n+P3DhtjjLEN2AaDQULkqJzjKq20OcxsmBw61O+PWSUkxFpIWknM53n60U51V3dVqWfeeut9632NrIPOtiC2aWO7FGwtt6wjDLBMm1/etphzq1Zw929eY3z6G9TWhNmSrsWQGiXXQ//RPJKVgxqfRhzF78eOxSAaBacTMsPRb8tKT1U3D5DzDjrzYwflNYE8x0WRamI7NKQEvSVBzWN7sT0GOIZ9Mh02VrGBVRLE2dKP5jORwkVmfgRnZhA9NITqHsRR3I/QUgx2j8OcnCbwq2I8LztwPual9JEiku+P4HWD7RDMKO6kfkYf7dcUsO+GAPtuDGJNUpgzdTdrdo6jz20xq7EVy5RMKQlR2pykdKNB6ZYM53u2061WjvawndXs+9pdGD6JlFlMaWMLG0vYmNhI00QP3E6Rv4j//Oz1nD/tJUp3vZsLehTOG2rgo4s+S3FV4WH3k8DQ5Q2oSYuCLWkKV2gkG4MoxcUouo4iQfX7UUpLeKrj56e8v/s3i43kOJ3JawJ5josZRYPsMC1UYRNc087Q9FoqnkySafRj+QVKSsE5aOHd2EdkYRMezUBMTcJkQWqhRU5SQHavm6snv8zTW65EFmexPxkiGi0DIWkMdtIXLiXYAdt7qyirijLznL2MHdtNT3chbneWyuoBLEPBcrv5+tQlfPv56xmq62JDRx3+Qo2SQIx40sWqnilQcOrdCN9JCOFg04c+zawf3oNZkoaYmlu7LzTZ8vF/xe84GPKhpKSAy6752mH1H+q4kB9++Bes+Ptasqqk/YoK3JEg7k1pHKZEKAqf/PItrIjuYPN/b0cIkDY8tPzraNrozMhP96WekZAXAnmOi3IrzjLTprowiByKkKyRlC6PEdwRAk3Nrd8aBslZDRSfX8Ku3kGaPL0kFd9h9xEOeGbxu/j6h//Mz755Iz3n+6A05+nRsqcR/wZBZBwENqXYVFFHzUCUQFGSsf6DmaRCoQAqCj95+moqpm7FNMeS3aOQbrTYXexG2OBttYjHBF949hfcfeW/n9KxeidR4POy59t3vvWFb8IXf/vW/ze3MH94d9HokvcOyvOOZltnFdOX9BK+MQVSIla0E50zDl+vgZKWoAoMj0q0TiXZFoc5Fl7NIpmxQT+4CnnJxK2IkhTfefy9BAyofjBBfIoXVIHenAQHxN8XJftCBd5F/Wxb18DMC3bhcueMzImYi80rmwh64vSoOg0BL2Om7mR5ei56D3g22VgqRMoVrEp4tLuLu0dr0PKcdeS9g/K8Y/ngRQvp/R8DMhmkplHZmSFdrDE40cPgBJ3+STrRJh3pAcUELSvpD/opW9PHoTt7rvHsZENPPUKLEVvYR3SiH2dSRY8qZBv89F2ic05TO2XBCO+rWI4Qkuf+MoeVz01m+bNTeOHRc7EsBT8p3GMHWTPk48rqXm49/xVsjyQyVSU+UUUgef9lLzG+uGMURy3P2YSUAlMqIzpOZ/KaQJ7j4oqqc/nsla8y9pldpKbW4N3SQaLCwtOvomi518rWJKkicA1Iglugo7iQj717BcuejbN7VylKRvClkltRaOO+j9zLF3e8B/GePfS1VwEq6Akun7iJl1fOYKZi4Fcz1Pj7GXQE6O3IhQQVis2k6fvY2lxH1k4SUC2+23wxVhpuu3o5TjULCNyqyf+1zGNG/RFJ7PLkOW7yy0F53rG4dT+yQAEhSFX5cEXKsAstEg6JNhy903RaSL8NUkE1LEoWq/wgthDfejf2cOAtX4dJvHwC//xENc69LrQrBrlh7iqKdIP1AxUsC09HpjQ2TvDT2VOMsg/mTGlmyOHBNFXKCwbZ82ATzjEmPtWkvmiA4mwErX6QJzvHE4sXAgKUBOeP3cyajqrRG7Q8ZxUn0iYghLgP2B9+f+pw2Zum4hVCfAX4CDkPi09LKZcOl58L3E/OOXox8Bkp5TGDauSFQJ7jR4Dlc6EaNgNTilD8UWyHB8M4uPdf8UcQoghvIEa8swTnWucBAQAQWxChdGkRA04/+lVtWEtqearSi1Vmo+7RURIqvqs7ifZWce+fr8HTnSa6OghCoqg2IbMaAF82SXeNwsZkEVNrEzS31jC9vI3GhmYMHNiGxfZ0NRbu0RipPGcpJ1ATuB/4BfDAIWVHTcUrhJgM3ApMAaqA54QQ46WUFvC/5MzmK8gJgauAJcd6cF4I5Hkb2HTc2khwUxZngRsZ86D5MtiuAxv6kQMFeNJJvOMNtHVhBpzlh93B02+SuG0AZY2PS0v28tLVIFN+FENFzEngdsVo8MTZVxAjFHFTERvegYTAtlT2e+ip0oTOAmQ17A5n8HhgY+94BjJhPA7oiOokMiVIO79hLM+J4UQmlZFSviKEaDjiEUdPxXsD8JCUMgPsFULsBuYIIfYBASnlcgAhxAPAjbyFEDi9LRZ5TmucQuIcUBhodCMdEunJYiV0ZNKJjLuQcQ/4DaSuMtDlpvirA6AdrpkmY24cVoyJVzSzMuxjYkkcrytLQSCF22kwvijFDSVryLb6KX6yGW9ZDGHah91DZC1KJvdDXRKlswgUnUhHNbUlYdojhewIlWPipCrYjrUvvxyU58TxD4SNKBFCrDnkGImT62eBu4UQ7cCPGM7PAlQD7Ydc1zFcVj389xvLj0leE8hz3KTTGiUruxEX1aDGDURMRfoNND33Qy8UMGIqSdtBMhGk3DuAoy6Judt7IACcZ7WTTLiBbb4MH731EZLSx6K6BDusWsqVfgodcf5v7wWoIQdaJI23q5WEP4gxMKwRSIlaJCmfPQhrKrDr07h0N+khk70tdWiBJJpmkcm4aessRDAagcbynI1ICebIk8qEpZT/aNabN0vFezT1Qx6j/JjkhUCe40eRGOUOClaHSM4soWiFYGCexNyfWAaBSGlYUqClVNa11uLTNLRSC0/SQrFAcyeJXZBGWhb/9/y7uPaidTzaPYcij0kmPZZSxwBfHrOUu/80BqsuiBkNsfDrK9j67DjC7YUUVUWYeuVO2hUfatqDZQ8xFPEibRXRL5Ch4gM/+0LJIPUzP9ZLntOHk+wd9EGOnoq3A6g95LoacktFHcN/v7H8mOSFQJ63gSA1sQzf4r14VlgoTo1sYTHxyYBmIzIK3u0a/mSCFD5SnQ5EVrDoXauY1HBQa73no9fTN1OHC9fzYuc5/Hza71huTKCEKLpMceczd1Ca3Y7jqhp6X1GxC/Yy69YtqMLGlgomCh1bS9EGDexwEf7nLdI3D5DdHUSMjYLMaSVWlw4Oc/SGK89ZxSlINN/F0VPxPgH8UQjxY3KG4XHAKimlJYSICSHmASuBDwBvGVQpLwTyHDduwyYpTMLXNeFvjuLsTFGyIYGvy4vtAiUD+kAG/+Q46UEf7vokNZHYYQIAIFbuxNEtMZbPIjkmykee+ATUZCALotcPFQPY75fom9JEJjTwfIfO5bXNONVcLKDdQ6U0m2UEH28hfuMUCgcN2ppVRGMMs80HGQ1UC1GZRjgzozFUec5S5IlzEf0TcCk520EH8A3eJBWvlHKrEOIRYBs519FP9rMfQwAAIABJREFUDnsGQW4J6X5yLqJLeAujMOSFQJ63QbA0Q9fMNGwoIjTXR+lrKt6+BI6BbM7lwAbFtvHOiTEQLcHry1AydGSy9+gEi5IdGkavTcVVXfRES8gkNYRm4xw3QI0vTL/hRq9KMK5hgNeHGmiNFlPpjRAzdAbSPnTVwFfrxh7I0F2j4ZwaJ7OjDN+kCKoAKSAa0aA5cGRH8uQ5Tk5UADkp5W1vcuqIVLzD138P+N5RytcAU/+RZ+e9g/IcNy41gEgkSBVL0jUGPVNtDEWiGiZqxkQ1TKRlM7C1BP+2AcaoUVrNkiPuo88MkXLYOE1BQJqoMsHE6j4mVIdRZBK/bnJZ6S6UUpPxs7opdCfJ2hqtsWIG0rmAdEXuBP7vJClavAVjgkHmVT/uiRkS6wtJrCslvrYUEXHgnJFh1m9+eKqHKs9ZSC6v0ZkfSjovBPIcNzMKxkFZBZmxAzg7sxhNaezKAJlyP0aZj2y5n2xlAcmsGxGRXONsI1HgYPmaCYfdJ9VRRGyyia2CKdM4XRKfmsWvZfG6DLpTfiLoZPs82LbAbZgUe+OARBE2lYEhZFwl/K1gzmFI1VBrvEgZwTkjjWNmFH1mFGcVOMUQ/am8XSDPiUBg2cqIjtOZ/HJQnuPmk9Pm81jLDsS+AgzTxtmp0naZi4pVBnrEQgpJvFYlPFEwrkfQs62Q/1rwCnc+fC1b2hqpKQ8zMOQHwFu7jcjQTGYGk0wWu1k7WI+BxvkFrRQFYvSaPjoiATKLpzEUCuI/b4ipJd2ApDdURHKVn2DvDvB5Ec0K1kQNq6sKZ3kfRR4TKaAnqpMJV4Gwj92xPHlGyImyCYwmeSHwBmzboH3H+3G5V2NZGmluZUzTt5BS8qWnlvL8+maCS/pwx2xmzxvH535wKwVF3tFu9qhQ4QmgKAZ60k26dgDRUgKBCD0XFSBsBwiwhUTxRulZ6OOpp8fhaUhgGA76hwroHyrI3UiPky6zsMqyaFoaGydjgoNoQAqVjOlicFBS5h+gfXs9Ba0G2VCA7eMLQUBgj0XpXovkrHL0J3bhNgzY5iQ5V5INl9FpHdhWDC6RS3aSJ8/bJJ9P4Cxlb/PFFAc7+PnW+Ty84VzMtAOP/wdUBCrY1dFL0wMhhKJgAa+t28Pyhd9j8YrvoI5SZqPRxO9040tLCraahCcKjPlh3IpJps2N3phASEi3+9BKshSXpclsCfDrv80j0JAgmjgoOD93xaP8z6pbGDtlJ0uWTeW9F29Ex8lwhlpcapIV267gX2Yvp9lRjiPqoGCvhb/94LJO1iegLJdIvuJvzez9+HRIW7kEN/vzmWcEZCzUXae3ep7nDEHm7AJnOif92yCEUIUQ64UQTw5/nimEWCGE2DC8fXrOm9S7SgixQwixWwjx5ZPdToBkoouigg6Wdo7lD8vnYSadYAsSQw6ah/qpfqwnJwBcCt2XFdN9ZSkdV5Ry6Vf/H6Fo/FQ08bQjLh0IC7IvFyBXBXGqBuXTujGFTVaRlE/tRCPL/MZdSMtGzWrMnrgTcciSzBq1kS9d/Bh6JosSyPLwy+cTjuk47Cybesv57SuLqKxuZ+1AA84dOrbfJtKgYg9PYdKFgmiDA92fEwpqIk31KwMoe1TIClAscFiQtVHSOrUvhUdjqPKchfwDYSNOW06FJvAZYDsHAyEdNTLeoRWEECrwS+BKcrvgVgshnpBSbjuZDe3r30dQg/u3zMn5FB7WKHD2Z8HloH92ECPoOHBqQDH51G8e56HP3c5Xv/4IL63cg1kjcFwxQEK6iMdcXFo7jv9ZeD1+p34yu3DKkULSPkcgIjZyfArxx1oGpiUoPbcPS6hEu734niyh4D+24ZsaYajThRTwkWuX0txWi6ZaLF4/AW2WJKb7ubxhDy+3TmJ1x8Scl6kN/vJe5la00Mk4irYbOKdFCVslhKbmQlkrlsAWkorz+thy+QwKV+/Es3o3Y5o9dN08kXSdDk5Qwrk0lc4draM9bHnOAuSwYfhM56T2QAhRA1zLwe3O8OaR8Q5lDrBbStkipcwCD5GLnHdS+eCSzSQtjdZE8LByIQRqGnze3Gw/Xeo4ou7mnl5uvuVnvLBsN+qlScb+827CQwVkLQczK7vYFtvAjF//hGgmfbK7cUpxhwX6ORFsp0nJC34GFhjo271Y9zfCb+twP1lCzyxoSxRy7iWtaANpZEKwK1yJ5jSRCjieLeDFHRNp29TIS7tmcfPENdSWh6kqj1Bb2c+7GrbwdOfUXGJxBOOmd1FLGBBITUFYFucF9lBRNUhsopPBy6YBIKNxah5YR/3iKPVPJWhY0kvJ8xtJNgSP3ak8eUaIlCM7TmdOtibwE+CLgP+Qss8CS4UQPyInhM4/Sr2jRcmbe7QHDEfj+1eAurq6427okp07Sb84wPPTx1Na1E9ooPKw/KHuLph5m8FrPx9CWOXIN4ycsGBgMEbl7WFEdZZwt49LxrTw1SnP49UMbAlPdk7iI78p5s+f+JfjbudoYxgW7731FwxFkgCUCeiZMISYUUhS+hCpDEMXKQxl1AMbxtSiJLtaKxkjQth+nWJnnH3pEqg0sXQFx7tbCDw6HjGQoW8u/CUwl6rCGD41SdzSeLR9JqwrIFUWwjUzw1DKwy2ffpnuzSVEuryUjR+keEyUVW2NCL9CrMEmOHEMXRcX03TPShyrDyqQUlXouW7iKI1enrONvHfQMRBC7M+Ss1YIcekhp94sMt5h1Y9yy6PKUynlr4BfAcyePfu4Ze4fXlmLv91iQWkffXIjBTVreXDNRfTECmkq6WFvpoxVO6rITvDikgYpnIfV1y2Txgm9GFMT2GmV0Hof/98dj+IaDm2gCLi+ZjubdrwK5IRAXzzOYDLNuNJiFHFmvEyLrhlO0z7cXhUI/qkR6/YWwnVOXD0u0tUSV30Ul9MkGnNi7XWxV6nANB14tw+wfuk4Cj4WQoqckE0+HSQ+IQNFAuFJoa2ooLvBiVINVgq0DUFShXF29pbyy3e/wHdXzCNlOKmaHqZqem5937IFzX1VICSiUKFjUQl4Jfs+MZ/gngR6zCRd5CTa4MYqz7uI5nn75Gb5Z8b39licTE3gAuD64XV/FxAQQjwIXMfRI+MdyptFyTtpdOwK4wA2ZguJd/u5es5Kzm/ciWkraIrNTb//HH0hDbfbid8ahFQxxrBHkCNjo1dHQM1gpB3YCYVz9M4DAuBQ5hXtxbAsvrb4WZ7Y0owtJTXBAD+58VqmV1WczC6+beLxVO7Nf4PA6p1tUPLYGBzXdmBXG/hfaULdHsRSJQFLYGqS+NQ0bX2lNIl+jOmZAwIA4PxPD7J1bxoeqSEWLMK4sg9zTxBnmxPTaWM0RVCDSUQ4wOKeSi6ubOMPKy9k0eSN1BT1E44FeKF5KllNQ5gWUmjIAgtHVMWothko9yBsgRQSy2Gjh8/8ddw8pwd5F9FjIKX8CsNJEIY1gbuklHcIIbZz9Mh4h7IaGCeEaAQ6yaVSe9/JaitASgdVg78MNVBkSJKmA49moCm5WaNmSbRaDWXtIO7nwLwpjFadM/Kq8QzeB22GFjnoXltGWdkgWftIuwFAvNPD79ds4Mll23GmcmXhUJR/e/BvvPqFj53WGsFzq7cf/URZFGN7MbYdwGpWUS/tJTXgQ/ToyMoMjsIEgZCbaImKWeJD+I+M6Z/+ex2phUlKnvHQv68U35ReUmkXDmFgmwK5rpKCh1pZU11E9fk26oUm9y+7lP1Ko8efYozSR1uhE89gAcnxGcQ68IQ1si6BdEiwBK6IgvnWIdbz5BkRp/t6/0gYjX0CR42MJ4SoAu6VUl4jpTSFEP8OLCW34nCflHLryWzUf96yiDtDf6NGtVnrLOGFFz/I52c+w5hAH+G0H18gSaJUwx5XjGtzGO9DINUkqCAyAhwa/a0qUXwkIjqmS7BxZRUz5h5UYGIRnRceH08ksQktdXDNS40bJFpMNnR0M6v29M18NWZs6VHLxcYgkUsy0KujTkrjM1JcMWUzO8ZVUOcM05n0s2nZDLxuAapkIByg1I6iKge/QfaV+6CtkvCiJMHnVYyuKqiSWFlwhQXOtgTpWkhfVIJ+3y58/gAzFuxmKOJDd2UJKBmG7ilDnGORxoKsIOs1kS4NNQFqJjfaWSTqWfDFzTP6SMRh+bLPVE6JEJBSvkRu5o+U8jWOEhlPStkFXHPI58XkEiWfEs6f1Ei8QiGgJKkt7aezv4Svr7z5wPlbpq7l8dC5pGfbBFalUHwFCKnmLBW6AMMgOySgHpz7FAame/npNy7l8nftZNI5PYS6fSz960QG3V4G+uIIQEmYFK7uQ+/PIoFv7vo1Dz3+JTw+15s1c1SZXltLokDgjexPYiRBgh23UaISmXFRqHTTnyrmL3+7HMttsykhsCtNPP4EekcBSAMrrrG1vZKGsn48epZEWsdwuChZ4yD8Lihf1ku2sZjooBsk+NtTuPb207OgBmNunEikgZp2i+jPK3BNSGPHdcJby6i9rIuddeVIMwGKhCY32gqJLAXDA9igDUCWI5fp8uQ5Hs6G+UR+x/AwuqphBaEzXcAMrYt9RiVdagGWnVv3V4VgwYWbefn16diXKSh/D0MwAKoKmQxyIMLET2RZ2VuHmjAxd/qRMzIs/esklv51EgC2CgUfjTHUn5uVBteH0fuzQO4n1dg5wJfu/C0//9W/jcoYjISB6U5iQxaFOy2kIhicoCK8Jt4dTtJ+hdDuUpxXZrDPjQ6LCRssDZdtAgLTYeJr1uiZpLO59aDZx7lcIzw/Q8nLDgYvqaPw5TbK9gIip3KH51WQCigUxC0GLvVwvn87HU9UE3k9gFAl5XND+GdF0HsLMLcUo3VD5toYqekStcuBM6SAgFTAorTp9F1yy3MGkTcMn10IIXC1phHnKGw2q7DqTWr0EFZaQVElK2UFP5zwLMuWTqf6+gS9q1zI1tD+yqgzffjmJhF/s0BJosaDmGMUAvVJYhs9qF6bwvlJ9jZXMvniUta82o6r98g9A7te33OKe/6P4eqwSNeo9M05+Oq4wwpKDJy9UUR9ASIWpaI6hG1oqNIEJ7R31VCCJDpWULjZQG/zYAdN9FYH2BBpTIAP+oud6BkFcXEdtpr7ggkkWQ1sN8RSOsWlcba/3sTU9+7C7ckgVEl00MuqZyZTOneIZHsCSxOoS/2k6wRmsUWqKbeb2PWaYM337xqVsctzFnIWqAJ5IXAIZY+14K/ykr4gDU6wUMCV2xpeGY0xQY9QsCON7/wUmZ+rxF8oQLYpKONtPBenCShpKnoH4BIDc4uHwbYgIWcZgakJjIxK95ZKAG6cOYTW30brEgXFPPwtMh2n98yiYF8WZ8pJulAFAY6EjStkEy1XKNqUpr/ARcm9Cu3vq0Nqub6pMUHN8ykSDTqOkEXfpRpKSODe60Lst4yUK7DRRk5LYvZ6GPSq6AmJkGA5FQyXxChK85WLl/LfKxbxqUuW89PHFxAL5l5h95DN5y96mf/ouZCxr3cQn1YBRRLvLsChgAFZFeY2vIlxO0+e4+Bs0ATOfKvGCaR2oULLMwHa7x6PaR0+NIVGDAB32KBrbQkzy9tpvClE6Scj1F8dZlZxGzt6KvjGB19GplRmvG8L7qEhQBAN+0jF3ACog0n8yl/5v/c8TWKc74g2xMYGyBqnb7z72xdOx9tlEGw1Cbaa+NoN3D0JMmUW/ZeXUPPaIB0L3dQ8kWXsfWnG3pei8vks3fN8mD4AiXuvjZ5QDwoAoOpJE8aooDox69PYfpt0ELI+yBZKsl4TNW3TEDiHm8ev596+Gfz8w3/hq/WvcFfVMu6942Ee0xuRmkJqajW+zT2MdfcwfspWDNXEW9THldM30Lxx7KiNXZ6zCwnYthjRcTqT1wQO4d8+0s5n76/C226RsFSEAE2xEQKixQ4SNrjdUQa3FvF621jm1O6l3t1PVqpsj1Swo7uaidOWcVt4F8u0AOPFDtZHC7ADOUOvyJiUbd9BIqVCAaSmBbj+6t188t3rcDhshhI6H3zgZgZT/ZQ7ykd5NI7Ov33hOmbObuKbdz2MhWTKxEpWOYao/3OIeH0xbbdUMOP8ZWxwzUZJewCwdZNxkzfSvWYaslTiXhUnca7/sPv2TPEw5tEk0YkuUpZK4tIUJBWMrAJ+G6Sk6HUXl1f/glklO/mXaYOs3ncjXbV/Zze9/HT7dAJ2Cfs+/gn4OFxw1X8R7RY4OkoIKBLbKmR1tJSXn8ovBeU5QUiOjDF2BpIXAoewdkMZ33zfCu7fMIU4GhlTZ39a8r1KBftMlXkLW3j6qSlsbW6gM1RKIJAknXYQCgdQU4L+lM4HL9zKb350O03Tupjw59dpT1UhNQVPqJ/xnzV5bs8Urixv4b0Xr+fOOWsO+IoW+TM8/ok/klLfB5yeQgBg/oLJLF37rQOfL7j2u/RfXIFhZHH1WSRUD/Mu3ABSxZKCoDNDPAk9ONDdguQYHakcntvFHmvSQwBXVuIZErifdTLUqCB9AtEtKWmXWMNva6E+nkId6qcDzDtqG19/+pQEns3zDuds2CeQXw46hN+tnEFnmx8xLUW9pwsXMYbFPRlbYV+kBC52cH5lM1pCIdLno7W1jL7uQpS4BlmVP75SSzal4NqWoa20lN6PTmf+v/Rw0a2tlH+/kMXiYrJqisdjVXxy9pojAmQoArTUT0aj+8eNe88QqdoUDrcL7x6T1uem0Z0sxRYqHtVid6KEta+cw+bvfI57P/8JSsQgIhZDHmJVc6/TSI4xiQYsQlUmwqFS2Cso2SEpHBBYLgXbeYxG5MkzGsgRHqcxeU3gEAammvzy0Tnw3jhS8VPiT3F52UZcqklXystv/nw1NRN7+PD3m/nrY6ClFEgdrG+5bFrbivncv1yCklZwSIv2aCntLMiN9LAzUay/gOaMjxv8R387Epl1nEm5ypZu/QFXj/8CLR9XSRQ7cIVVel6qJ2SS2+pnCW6/aDoAk6or+M737+I///J7usIhUnYxuuYkk0xBmwPt4g7MnlKMiIY3AlIFNSkxXdB/ztkVgTXPmY44KwzDeSFwCHYgTWKOl4qCFO+reJL/t+smFiemEHBn6I4UUKg4MLZ7+U9lNobfwjmkIoaNPrYmMXw2VJi0/7WQ2+/eysa9pbTzxl22EnZVYEzaRl/ASaWePeL0U52z+XD1qenziUBVVZ7Z82MApJQIIZBSYtsSVT1S2ZwztpHHvvwfWLaNquTO19/5bQoSLqKRINpKhaEL0gz16SgpBalJZHEGtPzrmuc04zSf5Y+EES0HCSHGCyGeF0JsGf48XQjx9ZPbtFNPYKUHo9ikKdDOj9dfyjenPsJF1ZuZUNDOu8ctY978LXgmDxF5ogKh2WRKLbJFFpkik2yxBQKUKpP+y/3cfM4OxhkJSoly8E2RlGdiFKSCPLx5Drc+eh0ZSzn0NNsjhfzs2aZRGoG3jxiOfSSEOKoAeOO1S9t28v21L/CZ688l5bXxvurGVNMQ16DMxG5KIWvSoKnoa/PrQXlOIyRIW4zoOJ0Z6dTq18AXgHsApJSbhBB/BL57shp2Ktjwwhbu+9ofSUSTXP2Ry0nV2wRXOXi1cxbjLlzL97ZfzezKNgodKUJZD5XuNJUTQ1TFYjwhpjJge7H3B6KxwKmbZLdoaEkdh21y2/UbeexTUyhzZskEFPQhG9Vw0n9TM0q/zkC8gCmLP8ptdZu5qLSDn+88l+2xMtyZ09dF9ETyqVcf56nW5gOf1ZSKGdQQ8RhFm4NkVMiWAinwJEAz4I7HPsyDN/129BqdJ89hnN4/8CNhpELAI6VcJQ6PcHlG/1I988BL3P2hXx74fM9dD1DW4CfyiXFQGaPvR9Mo+vxentsxAzvmQbjTTPMPcKO2lo7z3fi/rmLekCZmuUCC7jQoXiOJhotwphSWLy9iwoVJau9opeuvNWghFeG0Kb2qj4KZA5R9sYD+C0oQpoM/tU3jT225bFjY4Bg8+9e+14Y6DxMAAMoQ0GPjaaylP2FSmtRw9ea8iIQNfeUGyxL5rGB5TiPOguWgkQqBsBCiieEuCyHeA3SftFadAn7x7785osy7L0ZoSDDj3C42Tx9D5sVxzFu4kaDbJmXCeMPg3q2XcP38jag9aQKLC/FrFsIJpJwIUxLJSIRD8pNfT+Nz7vUEpkYZCoTp6CzBlgJ3pUqZblLaM0RkqBz/lizJJh3TI1HT4G61MeqPHob6TOPp5zbz4188i2VZvPfm8/jXD1164Nzjza8dcX32sjhIneIlChd89jWeWTUfEXHk4gcVZbh69kqiQ2fBty7P2cNZ8DqOVAh8klz2rolCiE5gL3DHSWvVKSAZTx9VkXO2DLAnOYHC2V14flxIy/aZZIs0lDSkGnq49X2reeWZaRTU9hJJezCcOmRzSmGlsxff9b1EtuvccEkbP/vGGKyvlbNj58G0lzt2eTBNhfl3bKTrpSoGbg7i3TeI2u/A9hokZ7nQt3hO2TicLD7z5T+xYUPbgTH+w0MreeSxNTz3+F28umsNe+UzwLjDKzX7UM4J0X95kOfap7FowToCIoMAIlLnud3TMfrOgm9dnrODd9JmMSllC3CFEMILKFLK2Mlt1slHuhyI9OHJTSTgFT60fYJwaQ1D1zpwJsDXnCBTpBENOVjxyDnsDlVy7R3LWVT9AI8+OJ9wuICpU1q59ra1PNszifi0QUwBP7tvOze8NPOIZ7e3l3PNZJWr1TX8eee1ZAJOmG5B3Itrk4oSjZyiUTjxRGIpWvrChwkAGI6SmrHY2NfJ/654gHSFjuowsYxDXsGGBCXFcUKWjxpvN882NwCu4ajVNrdNXM2++qJT2p88eY7F2bBZ7JhCQAhx55uUAyCl/PFJaNMpITuzAX3lbsTw/6IE7KYqCgszhGokJWs99J0Dcgj6z/MibPCGIuxbV4U92Saa8VJaGedjX3j2sPtGkm4ebT6XumCEhZd0Iq0jZwqmpWDsmMPEK/+G+wv7sM9rQNuTW/bIujJU/mUHnGG2z1Q6y4IP/IxIHaQmpKjnSE8eAdz+p98yw2PjNQ0cTgtNs7EtgVAkGb9CnbePypoEXo9B22Y/C6dtwuuweHbPRB5eNhtXWS/MP/X9y5PnqJwgzx8hxH3A/rzsUw8p/xTw7+RssE9JKb84XP4V4COABXxaSrl0uPxc4H7ATS4fy2ekPLaoeitNYH+AlwnAecATw5+vA14ZYf9OS8ouMxlwn4No7UYYJnZdBY4KQdPFe7H9bqJ2JVrSQ7ZS5NakAffsNLJHUFHWw/o9teyZXEZTYd+Be4aSfta11UO7i5AUfLdrBoXuGMmsfvizA0MsuA4e/sF4Cpb34N3cT2JSEC1mULp9CM1z5qmYl73/pwzWgtAFetHRE7lLIF0m6X89yPgZe1A3KdjFNsp+D6tYmjUtgpoqwcZV5Vw+Zz0xWUQsDVPrenFWxXh9yxm0gSLPWY84cZrA/cAvgAcO3FuIBcANwHQpZUYIUTZcPplcyt0pQBXwnBBivJTSAv6XXLbGFeSEwFXAkmM9+JhCQEr5reGHPgPM2r8MJIT4Jrkk8WcsY87pw+XL0PNqBbah4ipL0XRbC9leHbvNR9HlnShryxkKB7D8OUGwftd4bhnzPOtaG7ALJL/acxEzPZ2MLeylPVrE+kgdewdK0FKQjjkJGT4Gkh6qC/vpGipESkFFcIhE2sHagQT/8dXVfKFrGrseg4KVoQNtu/LLF47ewBwnUhOoToVkUxy3AqYKmnXQgU4ClgbOfoi2uWh7qh6t34Uoz5Kts1BSArYUwfRJ1Lhb6C8xef71WVRN6sTntOnod5PsaARvPitYntOEExgSQkr5ihCi4Q3F/wb8l5QyM3zN/hnnDcBDw+V7hRC7gTlCiH1AQEq5HEAI8QBwI29HCBxCHXDo1tYs8MYGn1F4Um5qr+mk6vJuzKSGXpjrnqNHEG7ogSEvU+duZXNXDR3raxASHDUpXqyfjvt7WQpuGmJ9bCw9pp9gooE0Gh2JQtwFabSIiVGlEllZTCrmotXhpNIZQRWSzowfaam09Yxnvfd57v7FZu67oJGX7isGB9R/fByf/9inR3l03ga2ipQmctDACmiI4cQwlgJ6v4F3m0WqWmdgdZD0heAMudH6wNahuN9mYK+fVc4aHM44WnWUbMpBR9JHkWeQZHGCoC+KZdv84ukXuXfxMjx1KZwqKMvdVKhBfvnLD1FS4n+LRubJcyIQJ9swPB64SAjxPXL52O+SUq4GqsnN9PfTMVxmDP/9xvJjMlIh8HtglRDiMXKy7yYOUVvORGqDRWzviuKoyqAOh26w9ulUBVPYiQJi/QU8Hqphfs0ePvSxhxGKpCVcyouvTsEq8RB/UOC9O0047iNkBwCJQzdxB7OUl6XJbFZJqkH0EpAulR49mAupYAn0AXDGi3kifj3L42sYc22ES69MsyHVwDdmfm10B+Y4EaZEGBLZbGLPE/Sfr1O4x8Tda+bsum5BZJIbY0ICZQCMLhvba5JyuXLxV4QgGbYp2qugLEgQeqmGsvP20heqB1SS+KgobaOnu5E7X3yCPeozqAuKCWVyORnUm21kRy/vueOXvJSPIJrnVDFyTaBECLHmkM+/klL+6i3qaEAhuVC55wGPCCHGcPQdavIY5W/5kLdESvk9IcQS4KLhog9LKdePpO7pSlH1dD6uPMML+6rpMdzUORNcPWEnP1o9gy3t43j3FctJNNezoquJtX1jkJZA0U2uHL+D3qyf/t/5cTuzqEEby1JQFImiSDTVpm5uD01N3Tz6wyvRoml8Ho3AuAH0wixWUiW2qRB3KMVXb/o6T3e/zNrBTdT6qvnppGvwaWeme6hhQNwVB9tBpl/im9dPv7MEb6mOYkPGLUk0Gnj8oMgsRrEf6c4g48qBV9dqymJtcZN+tIbCHQZ9NY1QlEF3Zckagp5QI4XPmXRPX8JQqoBoxn3g+ZZUCNU5KHXNZzPOAAAgAElEQVTbfP+7j/HVr980SiOR5x3F0c1fRyMspZz9D969A3h02LC7SghhAyXD5bWHXFcDdA2X1xyl/JiMSAgIIeqAMPDYoWVSyraR1D8duWxSEz/961jOLYmycEwne/qK+O8X51JU2Y1m21T5Bplb6GNngc3O3ZVINFQ9xaoXJ7PoPSvY1l1P2uHEzKoILfcmKMJGVS2yikBz5QSwf18a161xFEfus+I3KTw/hL/VRBUK11Yt4NqqBaM2DicKIyAR/Q5ko8S7zoXp1XFeEGYo4gVDwVmcpSCTRYRcGD5BoNdg6A3Z2yqnbaEncx5la6Dvnzopf6ia6GQvdqFAj4KnPUXSqZJSnCQzR3ofZSwHTE2xcXP7qep2nncyJ3+fwN+Ay4CXhBDjASe53+EngD8KIX5MzjA8DlglpbSEEDEhxDxgJfAB4Odv9ZCRLgc9xUG1wg00AjvIWafPSAr0WSy8ZJAdbQl+t62B0uJBFi16hV/9/hq0kJP2RBEr/7OGRT/dTsCZIR3TKSiNsW35FHq2FHPVZ9eyeMskDL+GJXOJEhUhqcgO0WnqTJVgXRRDiXBAAOxHCHi9ppmPjU7XTwq+nWmcBTqhACSaMqBbeF4vw6OTm+nvc5POSOpmd9KRKCBZKQ7LJwDQ3dIIeoJ0oZer5u9gyZ46indk8G2TGE4YqHWTvjQ379A1gwMZf4ZRhA3VBotqJp+aTud5x3OivIOEEH8CLiW3bNQBfAO4D7hvOHBnFvjgsFawVQjxCLCNnOvoJ4c9gyBnTL6f3O/0Et7CKAwjXw6a9oYGz4KR/YYJIVRgDdAppXyXEOJhci6nAEFgSEp5xI6qYUt3jJwfrHkcqtRbtEthZvmv8Ti+S33V67i0KhqCP6B7+SaUCnhi2Twa1qxgyQ/nUjklxtjx3WxY0oC/Ik7HriIuWmgSWu2n7JIIIa0AgEIZJ7LESWqckw0lNViVJmrF0fXFPqv/RHZn1EkXK4QnQvVG6L5Aw3ZbJMcmIOpEzSpYug2BLIrbpiSVpHOyDyGyuW/R8GxKo5R5C1bzmjGVaF+Eue9excq9TfTHg6CYFNW0cEPddl5pqaPQBwMpL4Z98BWuCQySDrn4509fMVrDkOedxonzDrrtTU4dNTKDlPJ7wPeOUr4GmHpkjTfnuAK0SynXCSHOG+HlnwG2A4Hhuu/df0II8d/AsbbHLpBSho+njSPB7ahlRvk9h5eF1xOrtgm8luWyVRGeP28jiW21bPTWIZJJ/LMitAUa6N/toXBBlr2vFVPZ1YcibLpdpTjfJXE+lWBZbAKOxiyGmiVwhM1Gkn1DGoEzneLuLOGrbMIZL7bIcvmEZTy/4xIICCzNyPmHmk5aWoJMmB2m/1kbmRZkajOIhAaWAN3mtdVTUCYkeH39LSxctJKPnLOOqO1GEwaqbSINSXd7IaJC57zqvXREizBtlUJXkgJPis6e/I7iPHn+EUaaT+DOQ467hsNIh0ZQrwa4Frj3KOcE8E/An/7BNp9U/rjkswT3ZgjsyxLqLuDa1zsRO/ch1m1HNLcg63U83YLf7ZhLlR2hYkGU7pvKab2+Gs91FueVtqJNclD+x914MDDjKlIKxPCUQSBRkaTDZ0eQuP1kFYlnrUqm1kIxnLzUMQ1vVQ84k7kL1DTOiigNcog9z9bjwcDdoiAsiQwayOIs0meidXnQlRDeSVESlklQiaBKBSE1ajwR9llBvHoc9z6bnYMVVBTEGFcawlYEW8KV2H35nAN5Th1Cjuw4nRmpJnCo47VJzkbw1xHU+wnwxTfU389FQK+Ucteb1JXAM0IICdzzZu5UQoh/JbdDjrq6uqNd8g9RUlKIcyCLdKi0rChn4vt6uGNbGxIFBZvVL/gY2lRNpsnJvuZqLrpyC0OmB8NWCGgZtIxNvDuIqyGLTxtkX6KUaLcbvdRAV00MqZI0nDg3nl3pnQcn+HFtj5EttTBLHbh2eXBemOSfZj9PyipEFzG2RSrZvr4J26Hgs9NEkx6KX3CSmGhhBiSOQUFKl6SSNagYvL67kZKJFiGzEIC0LVnXVsnM+h5C/1WOc6JBS7QYw1Rw6Qa+rIm6XX+LlubJc4KQnLCwEaPJSIXANinlYTuEhRC3cIxdw0KI/XEw1gohLj3KJbdxbC3gAill1/BW6WeFEM1SyiNCVQwLh18BzJ49+4TI3GfWf4t1q3bx1W/9ioF/8lPsSCKwQSr4gikEAs2UpCIuti0dx8RLWtA9BkM9Plb/fQrlZQn6P+EjttFLtkrD+ydB/A4nqUBulurYAp41Z5cQMNPgrfaj6BpqKkval8BaVcfvJngoKLDBUhjq8+GOu2mo7SPzeBpHhQDpJ7BBQ9iAAvHZBpVVXYSiZdzZtIwfv3wTjtooCpDpGMvMiTtZ3jqLMdkY2u8DiPPT6KUmosuFstyFiERHeyjyvJM4zWf5I2GkQuArHPmDf7SyQ7kAuF4IcQ3gAgJCiAellHcIITTg3cC5b1ZZStk1/G/f8Ca1OZzCeEWz5ozj6afu5rubrmZvugivmsGwFeyxBq5AGus1F5MWbmXN/bNpWV2Lw2WSTTqRDptLr9/Iy8mJWIoAh6A2mKX7O06sWlASoPQLzr0hzGAsSqE/cKq6dFLJVjrQhiwqXksRr1MYmqrhbBhErCwnS27dUcdGnxzFOymOfFTDXtWDKE+SHl8Ow6konU1dVBphpAY/XncjH7v0GdwOi4jpYey0Hr722nXoSi+Nn4rS/t86StiNdCgIw0ZNZLDVM39mlufM4XRf6hkJbxVF9GrgGqBaCPGzQ04FeIvMYlLKr5ATFAxrAndJKfdbuq8AmqWUHUere2jI6uG/FwLffuvunHiqXRcSMl4maTsRAsrcUT7wuSp2PVeK21qL6/1rWLd0MumIC29NjHlXb6QkGCPe50JLWmBpvP8Dm7hfn0XnTi+WIphyVZj33rCZ5RtbuObCI0NNn4l8/PJJ/GbxNtqudoBiUPVKgK6LJMG6FK7xEVQLBjaVMpjVUDSbaFU5whMlfolK2QMtSE1HSWW5/F838ofXZ3PBuR0UEuOeNVeCVAABwoK0ynnTWpBxL7ZlonYPIHQnZE1sYZOtzRuG85xCznYhQG632RrgemDtIeUx4HNv47m38oalICFEFXCvlPIaoBx4bDhktQb8UUr59Nt43nHz4fFfY1fkJhZ3/RQVB++p/yIVnjo4Fx7f9yIb+3Yy6/aNlLtjhDNeYmknlQ6LTK+OVWJT8VSGP8+bxD3/8Th9Az401aa0MMGX/noFF9UZb92AM4TP33QV//f8NvSmAVQMuqngsjmreKF1GuwtAgVEU5pFdZvZE69HTRuk64pIN2Zp/Y4LvSOLGVCJd9fw0SvX83jXLPrDAb445wl+uPw6kCrCE+dHlzzJ5x/8ADPru8g0BHC+IbNFZNrZtcyW5zTnbBcCUsqNwEYhxB+klMedU1hK+RLw0iGfP3SUa7rIaR37k9jMON7nnWjGFUzmMwX3HFE+v/g69iW+y7laG+2pIsZ6+kj6nLSnytG3qqRmQ7rQyfYXirk1djOLxu1FEZJndzUysMXB3X+YNQq9OXloGqQ3FZEpS6LWhHnx+fMontHNvIn7AFjbXctzS+ej1WcoLksjpIBBDSpMMvU5g2531zhe9jsJba3C4e3hhytuZNHMddT5YrzU2cDn/347FTN30X9/E6E5Doq2mrjCEtsJ0UaVRGVeCOQ5NZwJnj8j4a2Wgx6RUv4TsH7YS+cwpJTTT1rLzgDK/JfhVX/Ga+kgPi1DR7YQWyh8YcyHWLJhPXJ5jP4Lg/ROcqG8ovDo1kaEJpHdOtMmVuJwqKPdhROKZku0lIqyxoN9bQI5PsnAnkqeW1+DADK6DdUpZJ+b/iaF8pdjDE0OYA0oCN0CKXC26uzR6pFkGDNtiF3b3LyweB6gILHRmkIUlUK/R8X0SUKzHbn0TsOJjtBGHswlT563zTvAO+gzw/++62Q35EzlIxMeYU/vl1g2tJMyt+Diyk/i8VzOw4/M50MXfhvnU/3IoQHSYwpQsoUoiQwLphfzjf/96Gg3/YSj9kvMKQJHn0Z2Sykzr9zGFrUOw8p5RQkBTWVddLzeiEirdF7rRYnbYKhIIycQs/4krj2FiLl97GipZMrkXtxT2nE6IWtAynaytUXHOcfGX5wi1uc/mOkOSUFphIQ5hFcLjto45HnncNZrAlLK7uE/PyGl/NKh54QQPwC+dGStdxZCOBlb8T+MrTi8vLyulCVtbxm76azC32UQmezABIrbNBIZmPH/t3ffcXJUV6LHf6eq8+QkaTTKQjmChESwiQZkssEB/DDwHHjGOOzaXu+y9j7vrs1be73YLMZmzQMv3mcjwBjbmGAvWUQJSQgFUI4zmpynZzpU1Xl/VAuNpJE0SBN77vfzKdR9K51qevpW1b117vgduOTjPzHtUCTNVNuTwBYKt1tE322mbW6UjlOifi0RVryE4q0uRfM9NgEzx9cQBDwPNu0pAitMqgzOnrqbN62ppNr9W0nBvCQfnraV1lSanBN6Ft4wPqBsrwS6uYgjf/A/2kOZMYK1lofwPAdrZgDZCq3xMFEbkq5LNACtjqB2DqqQE0wTqg0SjDuMe7yedK6NBoTdc1xSFwQofiFMvMwmWRxhc9Vof9QZ0qhnYdXmo+UOVfECrlv4FvvailBgfF4zO9tLeXvbdsYuKBvsj8PIdiOkTeBW4EvAFBFZ321WHvBafwZmDD9uPpCfwusEywtSV1eIBNopiqbp8MLkWF1UthbhapquZJjYkhY6rRjRqgTBDj8J4vTX4N0zAyQLlXCj4O6L4eVH8UIukooSEJtgNSRKhJ2NZWDB9MI6gpbH+qaxbKkZw8p1b3DZAjMavTEAsr0SAB7CT0X6L0D34ZraVbWp36IyhiXXVqyaMF5hJlNSTQm1IZuWaAolikWERFeEWNqiYzyUxlzsiUGau3IpXNeBeODkWtg1NsEWl3RhgGgKnGoLYhY4EEwBtjJ6fTt7FuSwxyqhMu6nlZCkIptinJZv2gOMgSFZ0A/heG0CrfhZPq8HyKRwiAC5IpI7nAeVMfpeToOQCtpQ6qIo+dsCtBUVkkwq2ApuLjhCqN3Ca/PYbZcQm5ykYE+YhgtzUPEIdgUINggdhTaFzQ7JgE0wBOoIoGBDbiDID6+4ju9+7UHap4eQgBJ0PRqLiukaFWD53392sD8Kwxg2eptF9AoR2QbsAl4GdtOLwQqMkWXNI9+gsFqxG6ClRAgD+ZsDBPbZSLONVWeTsy3I2h9/nXvPuowJ2wT226QmBQiX5aOhCGMWj2Xp6aVIREjkBAh0CJIGO6lYCSjNifLc9/8XZ1w4h5t/+DHSbpR4JIeq6aNIRwPMDWdHGg5jmNBeTkNYbxuGv48/2PFzqnqqiJxP5urAMLpb+8g3qa5v5ps/eZw109rIqVHCeyzmzxvDJy45lY8ungXABefM4bwPzaKlKU5BYQw7YJN2XYL2wWcnLr/nAfa3tfH452+kKBYlEgoSDR1MwX39ZWdx/WVn8eCfV1LZ1MZ3Pn3RgB+vMYKNhIbhbtKq2igilohYqvpipouoYRyhvKyI3/yfzx13OcuyKC49mGW8ewUA8OSXj78NgJuXLf1gARpGXxlBlUCLiOTiZ/H8jYjUcZwEcoZhGFkvCyqB3iZauQrowk8a92dgB3BFfwVlGIYx1Al+76DeTENZbweaj3d7+6t+isUwDGP4GAltAiLSTs8XPAKoqpquGIZhjFzZXgmoak9jAxuGYRiQ/ZWAYRiGcXRZfzvIMAzDOAZTCRiGYYxQOvR7/vSGqQQMwzBOlLkSMAzDGLlMm4BhGMZIZioBI1ut3l3JC1t3Mq2slMvmTScUOPhV8TyPN1/Zyt/++A8kCixQwQsIX7nhHG6+cMkgRm0YA2gYZAjtDVMJGEf4698+xTMbtxKIe+RUOty9P4Edd4jkR7jl68v41d3PsWeckJgQINIhqO0P9H7Pb17hF799nTf+468G+QgMo/8JfXc7SER+CVwO1Knq3MPmfRP4EVCmqg2ZstuBzwEu8FVV/UumfBHwIBAFnga+pqrHjLK3uYNOmIjYIvK2iDyZef+IiKzLTLtFZN1R1lsmIltEZLuI/F1Pyxh9b0ttA89s3Eqw1aN4o0P+u61EKtsJNnfh7mnmZ994hGpxaT3FrwDcsKABf/JCFgnPHexDMIwBI9q7qRceBJYdsX2R8fhjvO/tVjYbuA6Yk1nn5yJyIAXvvcAtwLTMdMQ2D9fvlQDwNeC9A29U9VOqulBVFwK/Ax4/fIXMAf0MfzD72cD1mQM3+tlTG7dgJyC3xsVKpAh0pA6ZbzkekZYusIR0jiDIoRuwhXsfe2UAIzaMQdRHg8qo6gqgpyF7fwJ867CtXAU8rKpJVd0FbAeWiEg5kK+qb2TO/v8LuPp4++7XSkBExgGXAff3ME+ATwLLe1h1CbBdVXeqagp4GP/AjX42sThMsEOxUord2XO28ECn438l5ch5osJPn1vZv0EaxlDR+0qgVERWd5tuOd6mReRKoEpV3zlsVgWwr9v7ykxZReb14eXH1N9tAnfh12I95SD6MFCrqtt6mNfTQfY4ckjmw7wFYMKECScV7Ej0H//8OH/5zxdxUmnmXzCfq/9+ElhCqhSC9XaPv/XpwgCBLnBDYKcPnedZoDFYeNtPWPezvx6gozCMQfDBsog2qOri3i4sIjHg28DFPc3uOZqjlh9Tv1UCInKgkWONiJzXwyLX0/NVAHyAg1HV+4D7ABYvXpwFbfUD5+7vLOepO59+//3qx96gvW0b1qixyBlx2ktDBJ8OYbel3v8fokGb+qU5lAXaqIvmYSfBzlwwqECySAnWpyFoH7lDw8g2/feLMxWYDLzj3zRhHLBWRJbgnxSP77bsOGB/pnxcD+XH1J9XAmcDV4rIpUAEyBeRX6vqDSISAK4BFh1l3aMdpNGHnn3whSPKtjzbSMV5SnN+BOeMFPvdEkrWdBKtTZHOsWlemENynEUirAQ3pEiUhbBc//F5JwKBOMR2ttM1IzIIR2QYA6u/0kao6gZg1Pv7EdkNLFbVBhF5AnhIRH4MjMVvAF6lqq6ItIvIGcBK4Ebgp8fbV79VAqp6O3B75gDOA76pqjdkZn8E2KyqlUdZ/S1gmohMBqrwW8I/3V+xjlROwsFzHHAP3PsXCAQYc10HbqdLsiSAMzFBTd7BYSNCtsPs0TVs3jyWwn3NuLlFiCegEEwL0pGgfUEUO2Euyozs14ddRJcD5+G3HVQC31XVB3paVlU3icijwLv4w/zepqoHuuXdysEuos9kpmMarOcEruOwW0EiMha4X1UvVVVHRL4M/AWwgV+q6qZBiDOr5VckaGrq3vir4KRJFFQQCtdT3VrAnec8T3VdES/unUhxtIvPzNmAHXa4et3HcU+PUPx8PYnREZz8EOGaTrA9vNNyaUsddbeGkR368GExVb3+OPMnHfb+DuCOHpZbDcw9vPxYBqQSUNWXgJe6vb+5h2X2A5d2e/80/sMORj9J13QCoSPKd7+RQ2B0kg47h4WLGjivdD/Xzz60Di4Z1cHoaCebzytHc9IE2x3is3MJNoYoDncg5jFEYyTIggvegXhOwBiiknGb0J2FFL9aRMmrRcR+V4wCltVCcmUu43+XZltL8RHr7WorINESYsHsbdgJIVwVRrpyiO0JYyWFxbO2ky7Jghy7hnEMB54Y7qOHxQaNqQRGMOunJRScCQFbsW0ld6xH4QvFhEsgFktjd8EvNs+j0zl4Wp/2LH60dik6MUnH9hjnL91EqlhxIpAoUqbNrGLOjG04+clBPDLDGBjiaa+mocxctI9Qq3ZXkjsT5LDOuKGIR3qXxcTxTezaM5pEyOWjf/wkV0zeQdByeXrPKUQK2inMSVJFHpfMXc/sabtpbSogL78DL5JkZriOUVMbWfTELay58r7BOUDD6G9ZkkDOXAmMUN976ukjKgDwKwVtT1JdXULtOVE8x+K0Odt4pm48v913CmPG1nLm9K20tYZ5u3UCC0NVVMQaGDu2mpK8Zk6L7qOtK5dAwKOzo3DgD8wwBlA23A4yVwIjVFVDE2dbCW4vX0txoAsUNiVKuLNuIUvn7aexrYi2lgDvNJazrKCFK5euBiCVtnnpzXk4UYtTxtZx/5bz+OLsF1EaASXthvib1ZcRG1dLdXv54B6kYfS3If4D3xumEhihQnGPfyhdTUEgc+9eYG60kZuCW9g1C5bNWs1dn7uK1tl5/GXDbEa9kSKSl6J5cy6p0jQlp7ZSvWE01alRvFQ3jWVj36MxlcOK6lMI77Zpz3GRxpxBPUbD6G9D/Sy/N0wlMEKdUb6HguiRjbdnlNZQGynAspQZCyuZbFm8+5/jsVP+t72YDhQ4d/wu1i2O8+4fZqIt8MeaRYhCtF6w1CLVEMY7svepYWSXLKgETJvACNVUHT3KHCFoWTQ5Mc65cT1TWuvfrwAOLgFvPjCdT1eshIhiOxbRWptInY2lFoqSbsqFoni/H4dhDBr100b0ZhrKzJXACLVvVTFtnWHyY4deDaxYW8GavPHECrtoq7Zp35Df4/qJziCzwymKJzfSuGXUwQRzQGRUF+Om7mf7KtMmYGSvvhxZbDCZK4ERSru6+ObfX0B1Yz6q4HnC6o1juPOeC3l6w2w+WrSX1/84n6q8XLSHXkQatGlzE0wKVFK+ZD92QQqJORROa2biWVXMrK7EGW2+XkaWU+3dNISZK4ERKtzYzJ6CiXz2i9cSSLh4toWTF4BggoSG+b9b5lKxrwlvvtAZC2J3OeApCGgwgBdUnqueSdGmNuKX5VA4vuP9bTtvuxQ1O8jkxCAeoWH0P3MlYAxbl319KTaNeFFIlURw8gO4ZQlCO9uYW1bLqrenc+nH3kZ2BCEA6bwQbn4EJz+CG1BKxnfy5KtLmTArSec3uki/6OC865L8dQr33zoomC14NaZ3kJHFejuq2BCvKEwlMEJ99Uv/k+CuNKFdjdjBRgLpJmKvthCZkeA7p72Imwgwbm47C6ZvZu7VlYhtg+cCiuSGKLq8ldZkhFUNs1n2hZ0Ef9VG59cSFKxu5ZN3bePP75wKpl3YyHKmYdgY1j77/Id4/HdP0HpPGVhK2S+auGnKe9QRw04or+8v52tffodvrL+QcdP30rEmDzvqkXdmG16By/ToTppfH01t2VS+8fRbpDwlLMJjz51FY2UOly6bMdiHaBj9aqj/wPeGqQRGsE9O+AS/jm1j6vJNTA03c0FeJSs7KlheMxsnT2mycnnw2blYYzzCk1NEJh/ak2hsfgtvn1LEk2/N4+kNcymNdtIQz8FzhKL2du791NWDdGSGMQCUId/o2xumEhjhfvjRm/jCyntYV1TB7+r9sSgqqsZSk5PktdWzueJzv+adV8cSHnXoKDFlXju7W0q5ZdnL/G71+VS3ltCYk0sg6ZHTmKDu0y6O47Dotp/SlZvGKwmAgKQ8/mnuWXz62rMG43ANo0+ZhmFj2Js1oYLffPgbLKqZw7i9FdzkLeOhG7/K0kQ7yaDN91+8knNmvsu8SBU2/rXvhGAjV5etY1Hxblavmc6M27ZApIuizR3k74rTPs7FCdnM+9P3iJc7eGUBsAUsQSM23938Bq7rHicywxgGsqBh2FwJGEwcU8q/33DTIWVj3EnEapuwVhcw/YJaxoTbOTdvK65aRCx/SMpgp7CybgrecxZF6w/+qJeugVit0HCzQNg+Il+12sKp1/8r6x+9vf8PzjD6iXlYzMhq119zCWPiCQgJWzdXABAU7/0KwFGLZ9cvwJ2RpmDjka1j0UqFtP9cwQE6tgOdGEcndRKf6xyxjmEMK9q7AWWG+qAyphIwerRg7niuvfRCbM/m//3yYnbWl+JmHh1Oeja/f24pgUzKiaP1kAjsc8ncQUInxiFo4X/lLJhuMeU/v9f/B2IY/cncDjKy2Wf/x9lcc/mpbN9Vx9OvzufBPW9SWlJDbc1c9u9LsGDxFiolBy8E9mEJSdUWAo4inYo3Oo7/49/9tpAgUXMOYgxv2XA7yFQCxjEVFsRYvHASixdOIpG8iIaWOOVl+Zz5mR/TsrmIKec00nZ5jOQfihHXQwDPFlKzU1yQv5fHvABWqY1FDwmIgG8/9gR3fPzKgT0ow+gLip9KZZgzp2JGr0XCQcaNLsS2LOxJSfYlCrl19BssvWg3iVs6SE7ySI716LwmSdkXGskPO1yzbANeUjjaNfFjL28e2IMwjL5kbgcdn4jYwGqgSlUvz5R9Bfgy4ABPqeq3elhvN9AOuICjqov7O1aj9wrPqKN5fyklViu5eyxmLqijZVYUV4WcYIr5kWpebJrJkuBqqAJO0cwfw8Gk0x4etmcuRo3hy9wO6p2vAe8B+QAicj5wFTBfVZMiMuoY656vqg0DEKPxQXk2sz6/g9dfnsZj7TP4YuQdWie5tHkhpkg7W1eMY5cUEKgpJcAWnLaZBPK897uLep7iNQTolK5BPhDDOHFDvedPb/Tr7SARGQdcBtzfrfhW4AeqmgRQ1br+jMHoH6PyumhK5/DiazNo9WI80DmHrSsqaHl2FC9tmMijedMhHMSxi1l6bhppjeDUR3CaLJwGG22IIvEgLGob7EMxjBOTJVlE+/tK4C7gW0Bet7LpwIdF5A4gAXxTVd/qYV0F/ltEFPiFqt7X0w5E5BbgFoAJEyb0ZezGMaSs0UzkXXbvmgQTQ+xuGcXuSBlEgS4BF0KtQjRRTxPjIAV20saLWn5mxaTgiYcVGewjMYwT4z8sNsR/4Xuh364ERORyoE5V1xw2KwAUAWcAfwM8KiI9dR05W1VPAz4K3CYi5/S0H1W9T1UXq+risrKyPjwC41iuqZjI1s5ypl+8l0AtSMKCtAWOBa4gCYtAh8XqysXMKtgFFV14toOVBEmDZ7swNkFRvsk3bQxjXi+nIaw/bwedDVyZad4PvWMAABoRSURBVOB9GLhARH4NVAKPq28V/kdUevjKqro/828d8HtgST/GanxAp0SX4NpK3awY0bRHoM3CareQDgtp8yuAYBwmjKvi8bWnkxNrQsYn8Sq68CoSyLgEgWgXlumfZgxjotqr6bjbEfmliNSJyMZuZT8Skc0isl5Efi8ihd3m3S4i20Vki4hc0q18kYhsyMy7+ygn2Ifotz9BVb1dVcep6iTgOuAFVb0B+ANwQSbg6UAIOKTxV0RyRCTvwGvgYmAjxpAxd+wMzs3fwcbaCeRIJ7F6JdgphDosQl0WwQ4ItStLom8jkSSxXGV0SS2xvC7CsS6K8mspyEtQHDQNw8Yw1bdtAg8Cyw4rexaYq6rzga3A7QAiMhv/N3VOZp2fZ3phAtyLf3t8WmY6fJtHGIzzsF8CUzI13sPATaqqIjJWRJ7OLDMaeFVE3gFW4Xcj/fMgxGocRdAKsMsrwdkTASdNtN0ldx9E65Sc/f6UH2nnycZixo3ppEDb8dQmGvHIjbnYwQh54QT7OkYP9qEYxgnqu9xBqroCaDqs7L9V9UCSrTeBcZnXVwEPq2pSVXcB24ElIlIO5KvqG6qqwH8Bxx3UY0A6aavqS8BLmdcp4IYeltkPXJp5vRNYMBCxGSduqexi03tzqMnL46zZO9i2u4KueATbdrEsh1nzdlHjzmFZ0as8tPci8kM1lOTGUYKESLG7tYBk0j7+jgxjqOp9w3CpiKzu9v6+o3V2OYrPAo9kXlfgVwoHVGbK0pnXh5cfk3lSxzhh//etC7EDQmFNkNdLpnLzRSvo6AxRUtzK1h0TeXHbfOJVHbxYvIT4tghMH0NHC4goruffqnR25B1nL4YxROkHGl6y4UQfeBWRb+M/WPubA0U9R3PU8mMylYBxwkK5DknLQwpsyPX4r+fPJ5XnfxMjzUC5Q7gmyI6147G6HOJ7Y9hlCYIhj0TChvookh7sozCMk9DPXURF5CbgcuDCzC0e8M/wx3dbbBywP1M+rofyYzKVQJbb8c5uVv15HRNmVXDaxfOJRsLvz1NN4MQf5aX6nexN5rGo7DIWlsw85vZcz8POdOmJFidwrDCeBCDfIx1ysBP+vMQEj0COQ6gmQMm8fTR2FpDcWIA0h3ECEHDAw4Mp5mExYxjrxzpARJYBfwucq6qd3WY9ATwkIj8GxuI3AK9SVVdE2kXkDGAlcCPw0+Ptx1QCWeyfP3MPK37zMs7s8biTRsH9L0K6HWvfPmLbOzn3FY+gxLl29CbmhRy6Ov+d/6w6g/85/9EjtvXc+m38+IlX2F/VxPhW4aLTZ5LMDxKyHdqKPbQmjDUxSagwBShpK4BTGSWc9CiNxqmqLCI8p43U3hxQC0SxxnYSCSsz/uonbLnrrwf+AzKMkyRe3zwEICLLgfPw2w4qge/i9wYKA89menq+qapfVNVNIvIo8C7+baLbVPXA0H634vc0igLPZKZjMpVAlnrrpXd5dflLuNPG4p5SfnBGOJ/U+dPpuq6e586v4oGNGwhb/hc5ajlcUvwqb1U9xOkVn35/la376/nGg08S3RlnzIZm0sk0T6/YQywWpnFRlIJNKVomBvA0QlcUsMBOQLBTsT1lX3s+dETw8juJTelAVRABQeloDhMd4M/GMPqE0mcPgqnq9T0UP3CM5e8A7uihfDUw94Ps2zyqk4VUlX9/5i7UE9yKkiPm2/sCOLNzSVxQwHV3f5nX9kx9f54lUNt09yHLP/jyGiThUbC+CVrbIZGARAJpaqXonXbsmWmCjke4VQk1+5PdpgQ7QF1oaS1AKpOk4znE28Ikumw640E6mqOQDhJI9ftHYhh9Tujdg2JDPbWEqQSy0H+8/kfKz2hBRHtsuBILsJT03BhuY4jvPncl9fHc9+en5dBumxv27yFSnUY6j0zxEOxoozWehxsAXCXUAaE2CCYBxyWdLyCCzLYgbkEqiJeIoMkQpANIZ4CNPze3goxhSrV30xBmKoEs9KeqN9kSHMVlNzdSWHJkktbyuXUEwi72/hReABzX5tU9pwDQ6oZZXTv5kOVTXjMaEHB6qFA8F1UlHREUJdiRJhB3CbY5OLkWalugDkQsQrstpCOAxDNTq014n/kKGsNYFlQCpk0gC6VE6eiKUnxelHmTamnakmbXmxU4yQBj59Ux8+IddOycRPWHi0ht88cDTloW67vGoOkc/lQ57ZCbjRUTlJYNIQpmJGjdcmjaz8LZCWoiLmLbJHMFJ3bwR921/KsOKxgguFJIL41jr4kQdIMoSjhqsf5OcxVgDFN92CYwmMxpWBaakjMZYvDAHxdT35HL5LMqueDrK7n49teYe/k2AiEPLxnEc2O4EYgGUxTHhDV7Z/PN1y+hvKjxkO1d96GlBHLTzP9KHXb04Lc+mOcy57Z6SPt/C5YFagNB/1/LEhCwAw4BosRW5hF1grgKBdWw/vumAjCGN/G8Xk1DmbkSyEL/cO4n+eQrP6Dr40rTnlzS4xsIWge/iNXxPEbXtbAnPg475NJaBP97/TkgSmFhOzM274RuY79PjEWY+LHtWLkWF/52N9UrchFLKT83jhXzSBVAKugRSVl+baCC+snWcWzIjSZwHCVVIITaoKhRSVpD+xLZMI5v6N/q6Q1zJZCFpLGSf5z4FjmxBOVt1bz4+hx2tJZQ25XHO3UVbHphKjl17diuMuWNDXzutJf46Ny13LDoVS5qWsu2ew522ky5Dl9581Eum/AOKxqnkY5ZzLyqnhlXNCBRjydr55M7pYN0joMTwP+7ED91omuDF4acUJylC/9IwXYlWqu4qqTGHjfDrWEMbYppEzCGpv2NL3JReRU/7FjInrVlfORLG9i0aiqVHbmML23ktHPf4tnbZ1PykSpSa5O8cl4O+QuS7K8L0rkrTOj84Pvb+puVv6W2NRf2BigvaeGp+gWAIpn/Ru0U+aUd7PeCOOkgBK33s5iopaRDHqXRDiYtaeH5JrDSQqhFcNLu0cI3jOFjaN/p6RVzJZCFcsrOx1GodfPpyi9m3Q+LmDVhM5des5LxJbtZ+Z0ykoFRhP6hisj/svE8oWWlTecuCykRKr7kkXATALy6bQdWwOORuy4g30swLtqMhaIIOYEk8wr2I5YiYZd0vkcq4uFElXTEIxVVvHyPCbEmJoRaiO0XIvVKIqhMmlk/yJ+SYZy8bHhOwFwJZKEpY2by0MqphCIe0TFp9u+dR/UP2pDmDrQghjeqiJx4nPj4GHwvyejHc2h7GSQqFJ3vsaR8O2nPJWKDnbRIFwbAS9JcVcC0WbVMyW0krRZR2yHtWVS4cd5Lj4G8NG44iOviZ5GzFaJp/rBqKnJaA50V/h9DVNIskP3c8Pmf8ev7bxvUz8owTsoQ/4HvDVMJZCEF7qw5E+o8qqeEKfhzEjc3D6+oACvtEWxKUTa/kW0XzWTSP73NsumbqJ+Sj6CUBtt5oWomn5+VA8BppVNYtXYXn7r1BXa2j6fJiRCx0wTEo8sNkvRsTo808O6EGDWto9FoCnX8C0wJeIgFXscotnVN5qrpawgH0hS2pfjv3y4l2WaSxxnDmCq4w/9+kKkEslAsEKIoCo11Fs2zIsw4p4r650ehQRs8JRjqYsz19bibptA1vYCnGudTFOzEEuXl2pk0PyBwvr+tS8fOZUX1Vsbkt1NnJ5iX08DL8bF4WATwuCJvDzvyS2isLcJL2EjQwwr79/vVsfASFpKwODdnE8/f+wk8T0h0hXGAQMA5+kEYxnBgrgSMoSrktCOJPELqMOYzzeQt7qRjXYxgsUPBhzuwYn7rrXOa0vZKMTU7KrAdF2dNnKYbS3DVwZYAYwryyH0jxjM7TmVdcQXLFzzCmbFa6p0o5cE4AfF4mgkkmyLgCWpZaDrT1GQBabBb4cnm80l3+g+auQEIOeAlTQ8hY5gzlYAxVKW2B4mXh7GqbUJz0jALcmYl358fFAerpYvJt1qkVidIvCZoewetnxjHWQs7scX/asyeNIZAEl7fO4Oc0nae7ixnUbiZsmAn9W6YtxOFRFIego3VZOOpokHXbxNwBCtuE7BcUo5DMNNryL8AULoWhnsK3TCGBwV6MX7wUGcqgSwVklK8sIO6UBvPpTTWiYufGC6Iw7aWUpZ8aDciFnEJ0XxRLqlRhRSutPni9INjU+dGw4inuIUWbYkQLckwL3mjDtlXa2U+RFxsLKTBQmMWCEgaws1AvpJw8gii7/etTogiJn2oMawp6PBvEzBdRLPU3FkWkrnbsr19FOvry8F18dIeq+rGs6ezlMJwJ05jkPjbJUSqw5S8GsBKuLzTcOiIdIVOK+F0F6naGBufP+WQefHmCJtXTPKfGehMIxEl2gTROggkIDlaiXUmSaQCtI+1/GlCiMTkEKefbgaZN4YxxW8Y7s00hJkrgSx104wWnloXIdBhsXRJFS83TeS56lkABC2XpYX7qdleSP0jY7Bd/2ugjkNoaoK3mh/ipm55Iy656k1Wrx3H1n3TeK9uMnXbSyifWU+iPczudeWkXcG2FK/AI9TlkRidSR9hQc4+D6fCpasmTCCa6TWkSn6qix9f9Y0B/1wMo0+ZNgFjqArvq6DksXVYkyazNWc0H7tkG1sThaQ9m1nRRuqbghTYFslTE9RvCYNCbIzHGddu4r3dpYdsa8nFQda6LkWPVVF/+USad+bRWFmAKFhJh8RMmwmFteyrL6V8dwKvPYQTE0IdHuSnYWuYlo+EmTipFqc+SHSiwz9dezPRYOQo0RvGMGEqgeMTERtYDVSp6uWZsq8AX8YfH/MpVf1WD+stA/4dsIH7VfUH/R1rNnn01y65a+vonDiRlhdH83pTiKmX7UbCLhtXj6bud+WUXFrDudevpq0hBycVoKi8lcauHMqa2g/Z1qLy5TDpbym6rgud20j1utFEml3UEhLFYU45ew/BeAp9oxh1Q3hjHIJ4eDkWgZoQooAoT33th4PzYRhGvxj6eYF6YyCuBL4GvAfkA4jI+cBVwHxVTYrIqMNXyFQcPwMuAiqBt0TkCVV9dwDizQ65OQgQemcv7szxdK4sYMPKBYcsMtauZVXVRGaW1lIQ7GRXawmphM2UeQ2HLBe0C7lt7nf4ZM3/4+x4FaWXbWH35gpEPGbPq0RqA1StLkU6FIJCsDLIoRQCw/+PxTAOocAQTxPdG/3aMCwi44DLgPu7Fd8K/EBVkwCqeuTQV7AE2K6qO1U1BTyMX3EYvXTjV5ehpYUE9zQQfn0LXvqwnjiuR2x2gqvGr6OjI8TmmtGMDzWxbNxGCifMOGJ7p5ePZ3ruKN6rHE2gwWLxue+x+Lwt2M0W1b8dR2p9DnZh/MgzI1VIOmDyxRnZKAuyiPZ376C7gG9xaK696cCHRWSliLwsIqf3sF4FsK/b+8pMmdFLUyaVcel3Po47ayISCGC/tw/21uImEqTyAiSLI7zx+EJqu/I4c9QOrpi0nik59WxPjuJzU/+lx20+eukNXHHp2ezeO5vNdy+k6menc17XTdz9w89jOYrblA/xtH92dODLn3JoHQXBWtP8ZGQbNb2DjkVELgfqVHWNiJx32D6LgDOA04FHRWSK6iHVZU+PkvZYnYrILcAtABMmTOiL0LPG1796CZ/4xBKu+t4DdI1OEfFcvIYoeBZYwt9fdy3zJl3J8j13YtOFSAFfOOWfCdvRHrdXEI7wj2ddCGddeMS8F1Z8m3POvYOAgLQmIBDAE6VumoVbEmPbv5hRxIwso6BZ8JxAf56enQ1cKSKXAhEgX0R+jX9W/3jmR3+ViHhAKdA9t3AlML7b+3HAoZ3XM1T1PuA+gMWLFw/t665BML68iLU//+Yxl/le4SN9sq8VL3+7T7ZjGMNGFjwx3G+3g1T1dlUdp6qTgOuAF1T1BuAPwAUAIjIdCAENh63+FjBNRCaLSCiz/hP9FathGMYJMW0CJ+SXwBQR2Yjf4HuTqqqIjBWRpwFU1cHvQvoX/J5Fj6rqpkGI1TAMo2eqfvtXb6YhbEBa61T1JeClzOsUcEMPy+wHLu32/mng6YGIzzAM44QM8bP83jBdNgzDME6Iou7w7/tsKgHDMIwTkSWppE0WUcMwjBOlXu+m4xCRX4pIXaat9EBZsYg8KyLbMv8WdZt3u4hsF5EtInJJt/JFIrIhM+9uETnuyE2mEjAMwzgBCqinvZp64UFg2WFlfwc8r6rTgOcz7xGR2fg9Judk1vl5JtUOwL34z01Ny0yHb/MIphIwDMM4Eap9diWgqiuApsOKrwJ+lXn9K+DqbuUPq2pSVXcB24ElIlIO5KvqG5nnsP6r2zpHZdoEDMMwTtAHaBguFZHV3d7fl3nQ9VhGq2o1gKpWd0u2WQG82W25A2l10pnXh5cfU1ZVAmvWrGkQkT0fcLVSjnxYbTCYOA5l4hhaMUB2xTHxZINop/kvz+ljpcdfEoAGVT3urZleOlpanV6n2+kuqyoBVS37oOuIyGpVXdwf8Zg4TBzZEoOJ40h9+KN+NLUiUp65CigHDmRcPlpancrM68PLj8m0CRiGYQxNTwA3ZV7fBPyxW/l1IhIWkcn4DcCrMreO2kXkjEyvoBu7rXNUWXUlYBiGMRyJyHLgPPy2g0rgu8AP8LMsfw7YC3wCQFU3icijwLv4ozPepqoHGiduxe9pFAWeyUzHZCqBTAbSIcDEcSgTx0FDIQYwcfQbVb3+KLOOzNvuL38HcEcP5auBuR9k36JZkPvCMAzDODGmTcAwDGMEM5WAYRjGCDaiKgER+YSIbBIRT0QWdyu/SETWZHJurBGRC3pY94nueT0GMgYR+bOIvJNZ7z+6PSI+YHGISExEnhKRzZn1fnCyMZxIHJl5d4jIPhHp6IsYTiKOD5yn5STiKBGRF0WkQ0TuOWydT4nI+sx6/3qyMZxEHNdnPo/1me9sb/vQ90kMIpInIuu6TQ0ictfJxDAiqOqImYBZwAz8sQ0Wdys/FRibeT0XqDpsvWuAh4CNgxED/qPg4D8M8jvguoGOA4gB52deh4BXgI8O0udxBlAOdAzmdwNYBZyZ+f/yTD9/HjnAh4AvAvd0Ky/B7zlSlnn/K+DCQYgjgN+PvTTz/l+BfxzIGHpYfw1wTl99R7J1GlG9g1T1PYDDT9hU9e1ubzcBEREJq2pSRHKBr+MnZXp0MGJQ1bZMeQD/B/ikW/NPII5O4MXMMikRWcuhD6YMVBxJVX2zp3UGMg6gmEyelsx6B/K0HLdL3gnGEQdeFZFTDltlCrBVVQ+M0f0ccC1+wrGBjEMyU46INAL5+DltBjKGg8GITANG4Z+sGMcwom4H9dK1wNuqmsy8/x5wJ9A5iDEgIn/BP9NqBx4brDgysRQCV3CSPzQnG8cg6B5HBSeQp6UfbAdmisgkEQngV0Tjj7NOn1PVNH4f9Q34T6nOBh4Y6Di6uR54RDOXBMbRZd2VgIg8B4zpYda3VfWYT8+JyBzgh8DFmfcLgVNU9a9FZNJgxHCAql4iIhHgN8AFwLODEUfmh2Y5cLeq7jxeDP0Vx4no4zhOKE/LycZxxA5Vm0XkVuARwANex786GNA4RCSIXwmcCuwEfgrcDnx/oGI4zHXAZ05i/REj6yoBVf3IiawnIuOA3wM3quqOTPGZwCIR2Y3/WY0SkZdU9bwBjKH7dhMi8gR+KtnjVgL9FMd9wDZV7XWDW399Hh9UH8dxQnlaTiaOY2zvT8CfAETkFqBXqS37OI6FmW3uyMTxKJn89wMYA5l9LwACqrqmr7edjcztIN6/vfEUcLuqvnagXFXvVdWxqjoJvyFq6/EqgL6OQURyxU8edeAs/FJgc3/EcKw4MvO+DxQAf9Vf++9NHAPpGN+NE8rT0k8xjsr8WwR8Cbh/EMKoAmaLyIEkjhcB7w1CHODfClo+SPsefga7ZXogJ+Bj+GdwSaAW+Eum/DtAHFjXbRp12LqT6JveQR8oBmA08BawHr9h8qf4ZzkDHcc4/Nsd73Ur//xg/D/B73lSiX/7o5KT7IVyEnEsBjYCO4B7yDyB3x9xZObtxh94pCOzzOxM+XL8PDLv0gc9x04iji9mvh/r8a9MSgY6hsy8ncDMvvgcRsJk0kYYhmGMYOZ2kGEYxghmKgHDMIwRzFQChmEYI5ipBAzDMEYwUwkYhmGMYKYSMIaEvswI2m2bV4rI32VeXy0is09gGy91z2BpGNnGVAJG1lLVJ1T1QMrrq/Hz2RiG0Y2pBIwhRXw/EpGNmdz0n8qUn5c5K39M/DENfpN5UhcRuTRT9qr4ef2fzJTfLCL3iMhZwJXAjzJ55qd2P8MXkdJMahBEJCoiD2dy4j+CP2D3gdguFpE3RGStiPw2k2HWMIa1rMsdZAx71+DnoVkAlAJviciKzLxTgTn4OXpeA84WkdXAL/Dzxu8SkSPSBajq65mcS0+q6mNwzDTUtwKdqjpfROYDazPLl+I/PfwRVY2LyN/ipxj/5744aMMYLKYSMIaaDwHLVdUFakXkZeB0oA1YpaqVACKyDj+VRwewU1V3ZdZfjj/2w4k6B7gbQFXXi8j6TPkZ+LeTXstUICHgjZPYj2EMCaYSMIaaY40U0308ARf/+3uiI8s4HLwdGjlsXk+5VAR4VlWvP8H9GcaQZNoEjKFmBfApEbEzGSnPwR/G8Wg2A1O6jffwqaMs1w7kdXu/G1iUef3xw/b/PwBEZC4wP1P+Jv7tp1My82IiMr0Xx2MYQ5qpBIyh5vf4WSjfAV4AvqWqNUdbWFW78NMn/1lEXsXPNtnaw6IPA38jIm+LyFTg34BbReR1/LaHA+4FcjO3gb5FpgJSf/jGm4HlmXlvAjNP5kANYygwWUSNYU9EclW1I9Nb6Gf4g978ZLDjMozhwFwJGNngC5mG4k34g978YpDjMYxhw1wJGIZhjGDmSsAwDGMEM5WAYRjGCGYqAcMwjBHMVAKGYRgjmKkEDMMwRrD/D8vNE9ws1jS4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib widget\n",
    "\n",
    "D6=[]\n",
    "pairs=[1, 2, 3]\n",
    "beams=['l','r']\n",
    "\n",
    "files=glob.glob(data_dir+'/*.h5')\n",
    "for file in files:\n",
    "    this_name=os.path.basename(file)\n",
    "    D6 += ATL06_to_dict(file, dataset_dict)\n",
    "print(\"read %d beam/pair combinations\" % (len(D6)))\n",
    "\n",
    "# now plot the results:\n",
    "plt.figure();\n",
    "for Di in D6:\n",
    "    plt.scatter(Di['longitude'], Di['latitude'], c=Di['h_li'], \n",
    "                #vmin=0, vmax=2000, \n",
    "                linewidth=0)\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('latitude')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jJKH5qa5fL+K1L+rm/u4C/hH47mvxfvddgG8R38r9s3g3cZMSpLvN339bjwXekxa/cMf9XAKt3NM5LzL9G4l4r3kErpV419ZX3OvReMXvpXi59jvceVaeMo1pfIg5eMWTj+MV0wWXdcW7GK7HC5RrgOnBD4GXC12EV2TxN7z6yT8Glt+CF0S2410MLnMH5Nfui19F/MWhte/3c7wc4U534lzl1vsFrnV0YNsH3Bcc/LsssPxk4G33XkuAk0NfSHjbB9yyU/AaUOxgf9HepMC2t+Nd3He5//cQaE2LF9Ducp9po/vMXUPfw3YiWvviXWR/7va7zb32H2kbjRe8duH9IF8Axge220CgJX4a3808Wgb0KrwL7jZ3/IM/ulK8tgDvuWPzGvtvvo5w3+9ml7a/453kFXhVEvV4NxOvASe5bf4vXtsD/zj+R2BfB+FdVDa6bRfiWvfj3Qg+6I5hWq3cXRp+GTH/K24fZYF5DxAd0MPni7L/hiTyO3Pb9sa7SO7A++3dTOgCHpGu/wWa2f8EyE5geWB5F7xHhj7GC/zfjbgAhdN6qls2yR333XgXnotD2/bFK0nZhdc6elpg2Q+JfzJlJ17RabLjNC/dYxF1jENp+wFeu5Pw8brUrb8rlLahgXWOd8t7RWz/o4h9/yidz5zkO0x6Xrh1niV0s5/m9esQvIajW917PwOMDCw/Gu+meA9eKW3wkbcX8W6Kgp9nTia+/7YeC7z653kuvSsJPcmT6rxIsM/2/EYuJv73No/EAf2zLs278Kqtngh+F8n+/At7Uq4yfiPeXWy4sUFGifdA/pWqelKW9/MO3g3KO9ncT2flqk5+rarHBeZdRgd8N8YYY1ov3Tr0vni5nawG844iIhV4xdMWzJP7Ya4TYIwxJj1pBXRV3aSqv8l2YjqKeq0HZ+Q6HflMVRer6pzUaxYuie5O0/9L2XCuo0nL7khjXc3mOm0mPdKyO1r/7+Jcp62j5eJYJPm9n5ytfWZSWkXuxhhjjMlvNtqaMcYYUwAyMsqZ8fTv31+HDx+e62QYY0ynUl1dvUVVB+Q6HZ2dBfQMGj58OEuWLMl1MowxplMRkXR7ZzRJWJG7McYYUwAsoBtjjDEFwAK6McYYUwAsoBtjjDEFwAK6McYYUwAsoBtjjDEFwAK6ybjq2nrufHEN1bX1uU6KMcYUDXsO3WRUdW09F9+7kH2NzVSUlfDQlRMZN6wy18kyxpiCZzl0k1ELa7ayr7GZZoWGxmYW1mzNdZKMMaYoFGRAF5FSEXlDRJ52031FZK6IrHb/KwPr/kBE1ojIShE5KzB/nIi87Zb9t4hILj5LZzNxRD8qykooFSgvK2HiiH65TpIxxhSFggzowHeAFYHp6cALqjoSeMFNIyJHAhcBo4HJwF0iUuq2+Q1wFTDS/U3umKR3buOGVfLQlRP57ueOsOJ2Y4zpQAUX0EWkCvg8cG9g9peA37vXvwfOCcyfpap7VfU9YA1wnIgMBnqr6gL1xpd9MLCNSWHcsEquPe0wC+bGGNOBCi6gA3cA3weaA/MGquoGAPf/QDd/CLA+sF6dmzfEvQ7Pb0FErhKRJSKyZPPmzZn5BMYYY0wrFVRAF5GzgU2qWp3uJhHzNMn8ljNV71HV8ao6fsAAG/3PGGNMbhTaY2snAl8UkalAV6C3iPwR+FBEBqvqBlecvsmtXwccHNi+CvjAza+KmG+MMcbkpYLKoavqD1S1SlWH4zV2+7uqfg14CrjUrXYp8KR7/RRwkYh0EZFD8Bq/LXbF8jtEZKJr3X5JYBtjjDEm7xRaDj2RGcCjInIFsA64AEBVl4vIo8A7QCNwrao2uW2+CTwAdAPmuD9jjDEmL4nXiNtkwvjx43XJkiW5ToYxxnQqIlKtquNznY7OrqCK3I0xxphiZQHdGGOMKQAW0I0xxpgCYAHdGGOMKQAW0E3G2XjoxhjT8YrlsTXTQWw8dGOMyQ3LoZuMsvHQjTEmNyygdzL5Xpxt46EbY0xuWJF7J9IZirP98dAX1mxl4oh+eZc+Y4wpVBbQO5Go4ux8DJjjhlXmZbqMMaaQWZF7J2LF2cYYYxKxHHonYsXZxhhjErGA3slYcbYxxpgoVuRujDHGFAAL6MYYY0wBsIBujDHGFAAL6MYYY0wBsIBujDHGFAAL6MYYY0wBsIBujDHGFAAL6MYYk4Z8HxjJGOtYxhhjUugMAyMZYzl0Y4xJ4c+v17G3IX5gJGPyjQV0Y4xJorq2nseWrEfddGmpDYxk8pMFdGOMSWJhzVYam71wLsCXx1VZcbvJSxbQjTEmieCwxV3KSzj/mKpcJ8mYSNYozhhjkrBhi01nYQHdGGNSsGGLTWdgRe7GGGNMAbCAbowxxhQAC+jGGGNMAbCAbowxxhQAC+jGGGNMAbCAbowxxhQAC+jGGGNMAbCAbowxabDhU02+s45ljDEmBRs+1XQGBZVDF5GuIrJYRN4UkeUi8mM3v6+IzBWR1e5/ZWCbH4jIGhFZKSJnBeaPE5G33bL/FhHJxWcyxuTewpqt7Gu04VNNfiuogA7sBT6rqp8BxgKTRWQiMB14QVVHAi+4aUTkSOAiYDQwGbhLRErde/0GuAoY6f4md+QHMcbkj+AALeVlNnyqyU8FVeSuqgrsdJPl7k+BLwGnuvm/B+YBN7j5s1R1L/CeiKwBjhORtUBvVV0AICIPAucAczrkgxhj8ooN0GI6g4IK6AAuh10NHAbcqaqLRGSgqm4AUNUNInKgW30IsDCweZ2b1+Beh+dH7e8qvJw8Q4cOzeRHMcbkERugxeS7QityR1WbVHUsUIWX2x6TZPWoenFNMj9qf/eo6nhVHT9gwIDWJ9gYY4zJgIIL6D5V3Y5XtD4Z+FBEBgO4/5vcanXAwYHNqoAP3PyqiPnGGGNMXiqogC4iA0Skj3vdDTgD+CfwFHCpW+1S4En3+ingIhHpIiKH4DV+W+yK53eIyETXuv2SwDbGGGNM3im0OvTBwO9dPXoJ8KiqPi0iC4BHReQKYB1wAYCqLheRR4F3gEbgWlVtcu/1TeABoBteYzhrEGeMyYkZs1fwzPKNTB49iOlTR+U6OSZPidcw3GTC+PHjdcmSJblOhjGmgMyYvYK759fEpq+ZNKLggrqIVKvq+Fyno7MrqCJ3Y4wpNM8s35h02hifBXRjjElDrvpynzx6UNJpY3yFVodujDEZl8u+3P3idatDN6lYQDfGdGrVtfVZ78Etqi/3juxkZvrUURbITUoW0I0xnVZH5Zz9vtwbGputL3eTtyygG2M6rUzknNPJ4Vtf7qYzsIBujOm02ptzbk0O3/pyN/nOAroxptNqb84513XjxmSSBXRjTKfW2pxzsIjd6sZNIbGAbowpGlFF7FY3bgqFBXRjTNGIKmK/9rTDLJCbgmA9xRljioZfxF4qWBG7KTiWQzfGFI1sPH7WER3b5NN+Tf6ygG6MKSqZfPwsV13C5rIrWpO/rMjdGGPaKKpOvpD3a/KbBXRjjGmjRHXyMxet4+v3LWLmonUdul9T3ERVc52GgjF+/HhdsmRJrpNhjMmgqLrq4DwgbvnMReu48S9vx7Y/7MCeXH7iIUybMDTr6eqsRKRaVcfnOh2dnQX0DLKAbkx2dXQQi6qrBpLWX5/xq5dYs2lni/caNagXFWUlXHjs0IwH987OAnpmWKM4Y0ynkIuGYInqqv15exua+dPrdfHpSJBJWrFxBwBv1nm5dwvqJtOsDt0Y0ynkoiFYZfcKSkQoCdRVTxzRj7ISAUCBWYvXcdWDS6iurQfg8pNGpHzfW59e3ub69eraeu58cU1sf8b4LIduMqq6tp4/vV6HAOcdU9Xp6/ZMbkQVrXd0v+vVtfXc8vRympqV0hLh5rNHx9JywfiDecgF5GaF5975kL+v3MSF4w/mvGOqmDSyP/NXb0n43nsammP17K3JqdvjaiYZC+gmY2YuWsd/PPE2Ta7E8bHqOh7+hl1wTOskClrt7RQmVf37zEXrmLNsA1PGDGbahKGxEgEFVJX63fti6553TBWzFq+LnesAjU3KzEXr+NPrdTx05USOHNybJ5a+T2X3ClZt2kFTc8s0zVm2oVUB3UaHM8lYQDcZUV1bz81PLou7wNkFx7RFMGjtC51DUZ3CpNNQLlXONtgy/WWXs05WIuCnY/Ha+GJvZf95P33qKKZPHRXb/w2Pv8mazbvi1t+6cy/VtfVp/0ZsdDiTjNWhm7SkqrdbWLOVpub4xkAlJUJl94qOSJ4pIJXdK/BPpWYl6TnkB+pfPreSr96zgH//y9uR5+ifX69jb0Pi+vdHXouvz56zbEOsROCzowZyxMBerHSN2nx9QukqERI+Fz5uWCU/+/JnkFC63tmwg6/esyAyzVG/OT9N3/3cEVbcblqwHLpJKZ16u4kj+tGlvIR9Da5cUbxiylueXs4Rg3rZhcekrX73PgQvt1viphOJy82HiryDz4zPem0d/u1maWl8wK2urWfZ+x/Fve/owb0BWLlxB3Pf+RCIb51eXVvPvJWbYuuXlwo//uIY6nfvS1hSMG5YJccOb5mr39ekcS3l/XYoj1fX0djU8jeXya5rTWGxHLpJKZ3WxX7O4fqzjuCrrk7QuqU0beHfHJYKVJRHFytX19Zz41/e5s312ykrLYnlfINF3r7fvvRuXP312KoD4gLiwpqtcVVFAB/vbQS8nHqQP72wZiuNrhhB8BrJTZswNOVQrDdMGUVpOJvu3sP/XBffu5CHF62L+8396fW6hCVk1urd+CyHblJKt97Ozzn4OQyr5zNtkarxW3VtPV+9ZwH7XBQuKxXOPHIg81Ztpqmp5Tn34cefxG2/tzG+ddrEEf0oFeKCuh9gp4wZHKtT96f9bYK/ifOPqUr7s0XVvY8+6ACvVf1fl/NJQ3z6pEQS5tat1bsJsoBuIoUbGrWmdXE2hqg0xSVZsfLCmq00BKJvU5PymYP7cPUph8YemQy68NihseJygAO6lQMwY/YKnlm+kcmjB/GNk0fw25drUIWKshLOcwHab4Hut34/YlAv7nxxDRNH9GvzOR6+oQBY9sFH/OipZbGblKCmJqUJRYFPGpq55a/LufkL3iN01urdBFnXrxmUy65fM9klpt31m3w1c9E6HnltHcs++ChWjF5WKjxy1fGs3LiDm59cRrNq3HlbXVvPl3/zKsEr3diqA1hat7/evLREaHbPm9/ypTGRj5Jl6ncR7uu9RGB8RK49Gf8zA3z1dwtjJQUPf8Prmraz3Uxb16+ZYTn0ApDpAGx3/SYfRQXCZoUSkVgw9+u1g4+7LazZSjjb8laoEZz/hEb4efOgTP0u/JuF+1+poWbLLlRpVTAH75n3GXNWMH3KqP1dzaqycuMObnl6ud2MFylrFFcAMt0lpg3NaPJRuIGa/2hbU1Mzc5ZtiHtsskQkdt76deRR2/pKSyTl+e7/LkoAkfY9kjltwlDOdcX6bS0jfW1tPV+7dyH7mrzi+KZmZc6yDbHH8/x+5k3xsIBeANoagBO1jrVnXU0uJTov/UfJfMEgPGXMYLqUe8G2zBWbBx/zevSaEzh2eCU9u5TG1bH36V7ONZNG8OjVx6c838cNq+Tms0dTUiI0u0cyW9uyPPjZ/N9tRKP32LwSvJKIqHXA60I2uNHowb1jNwgKPLZkfco0Wiv5wmFF7gWgtY3QUj3n6r+nBXLT0ZJVH/XqVh57Pl2AC489mCF9usWd88GuW4PGDavksWtOiL2/X+f8/bM+FStiv/a0w1Kmr373PppV21TsHvXZHrpyIn96vY7HlqynoUkRgatPHsGZowfFjbceXCeRpmao2RLfE11Dk3LH86u47ozDI9Np7WUKiwX0ApFuAJ65aJ3XRWuzxu7krZ7c5Au/Rzf/efI/vV4XC2z+8+nBR8WCj2/5dcevrd2WtDOj846pQvAeFWttfXN7ul6Nqhrzn1s//5iqFjfk4bQ8vmR97IYmkedcJzhBr6zewmtrt3Hz2aNbdHxj7WUKiwX0IuL3t94YqEAUWl9Pft2sN5i3ajOnHj6AOy46OgspNcWouraex5asjwWsqOevE5VEpROYwrlRoNXBrD2PZCa6GUjnCRW/I5u21LcrXn161BMAyW5QMvnkjOkYFtCLSLi/9T5B5RQAACAASURBVFKBi44b2qphTq+b9QZPLP0AIPbfgrrJhHDva6MH9+bt9z+KzNGGpZNzDgd9hTbltttaHRV1M5BukXfw8yktG/WlJMSqCvzGcv7nuPns0bGqCuuwpnOzgF5Egv2ti8Dpowa2eszyJ10QD05bQDeZEA7KFx47lJUfLk8r4KaTc67sXkGJeIXWfpF9VFF3NoVvBtIt8g5+vh17Grh7fk2r9vupgb1Y9aE3uIwCj1fXxXq3i6qqsKL4zqmgArqIHAw8CAwCmoF7VPW/RKQv8AgwHFgLfEVV6902PwCuAJqAb6vqs27+OOABoBswG/iOdvJeePyLgt8g7vkVHzJ/9ebIu+9ExW3hA6BpbGNMOqKC8hGDerWqh8Jkw6fe8vRymlznMTefPTphXXVHak2dfPDzDe3XgznLNtCvRwXvbdnFwN5dAZj7zoexRoO+0hJ4d8uuuK5tm5r2P94aFbhtmNbOqaACOtAIXK+qr4tIL6BaROYClwEvqOoMEZkOTAduEJEjgYuA0cBBwPMicriqNgG/Aa4CFuIF9MnAnA7/RBnm3303NiW++25LcZsV0ZlMCAflYOOt4HRr+TlOJXnnMR2trXXy0yYMbdGSv7q2nvmrN7OvoRn/YbbSEuGznzqQ51fsbywXbjcTFbit++bOqaACuqpuADa41ztEZAUwBPgScKpb7ffAPOAGN3+Wqu4F3hORNcBxIrIW6K2qCwBE5EHgHAogoEPqXEFbitusiM60VjolOpm6UWxrjrMjSp2CNzHt2Z8fhG/563LedN3a+m1m/M9eWiJcMP7guKq2RIHbHl3tfAoqoAeJyHDgaGARMNAFe1R1g4gc6FYbgpcD99W5eQ3udXh+1H6uwsvJM3Roy/6fs8V/llyg1fXgqe6+E138rpv1RuT7nfPrV/iPL4y2IjqTtnQDdaZuFMPnPBAbZCXbNxPpysT+xg2rZPSQA2IBHaB/ry5Jf+8WuAtHQQZ0EekJ/Am4TlU/FknUz1JkB0zhKqjg/JYzVe8B7gFvcJbWp7b1wsNHPlZdx8PfaN2PP9mPOFHAf/qtDZHrL637iFv/utyK6Eza0g3UmazL9c/5jr6ZSFem9nf+MVU87jqhKS+VuOf121t1YfJbwQV0ESnHC+YPqeqf3ewPRWSwy50PBja5+XXAwYHNq4AP3PyqiPl5ITx8ZDYuNlEBvzHJszJL6z6yO32TtnQDdTbqcnNxM5GOTO1v3LBKHr7q+DY9Hmc6t4IK6OJlxe8DVqjqrwKLngIuBWa4/08G5s8UkV/hNYobCSxW1SYR2SEiE/GK7C8B/qeDPkYL4Xq1iSP6UV4qsRx6RxVxl5YQG7LSmPZoTaDO9I1itm8m2loPnsmbl7Y+Hmc6t4IK6MCJwNeBt0VkqZt3I14gf1RErgDWARcAqOpyEXkUeAevhfy1roU7wDfZ/9jaHHLUIC7RnfXDVx2fsA595qJ1Cfu0bo+eXcv4aHdjxt7PFLdclehk82aivTnhbB0TewytOBRUQFfVV0g8MNHpCba5Hbg9Yv4SYEzmUtc2C2u2xvq23tew/846XCfmC44Z/fLqLQBtDurhGwNJ0kKgIjw+pckKe9Y/M7IVOPM1J2yPoRWHggrohaiye0WsNV6zm4bEOYHwmNFzlm1oU0CPujHYva8p4foT7I4/66weNP/lc07Y2rgUPhsPPc/V795Hicv8lgixDjGCOYF9Dc3c8fwqqmvrmTJmcNz24el03f9KTYvppiSN4tq6H5O+qNxfsZq5aB1fv28RMxety3VS4vg54VRjq3cEG+e8+FgOPc9F3fFX19bz/vY9lJWW0Njo9Qr18uot/GP1FsYPr4w1XisrgSMG9Ur43snq2vc0NreYruxRwZadLXvYKk2xH5MZ+Zz760iZrFbKhnzICVtpTnGygJ7nojrE8H+oZSXCiAE9WLN5F+AVyS9eu/9uXJWEdXipLoq9u5TxfmD93l3KOOWowZGDQjQ1Exu9yWRP+FyYu3wj1z+6lMmjBzF96qi4dQu5rj1T1Ur5IFvfUzbr8sf++Fm272mkT7cylv7wrIy8p8kMK3LvBMYNq4wNGxn8oTY1Kz26RN+TpRrn/K4XVyed/viThhbTNVt2JUzjrDwr+ixU/rkwd/lG7p5fw9qtu7l7fg0zZq+IrePnzn753EouvndhwRW5ZqpaKdey+T35pTmlktnHWv1gDrB9TyNjf/xsRt7XZIYF9E4m/EO98NihlIVamJeVCtMmDI0Vs0XVpW3bHR+ww9N7Qw+c721qZvmGjxOmqxk485fz2vahTKs9sfT9uOmHFtXGXhd6Xfu0CUP5yblHcfLI/vzk3KOyljvPdh10Nr+nbNXl+8E80bTJLSty72QSDTH5p9fr2LJjLwN6dYl7Lj1RXVpl9/K4VuuV3cvj9tMQCujh6SirNyfOwZvMquxewcaP98amd+xtYuaidUybMLQo6tqjRhvLpI6og87295QPdfmmY1lA74SihphM9MNNVJd27WkjY3XoANeeNjJuu4ZGbTEtat3EZVNr6lN7dW35073zxdVMmzDUnjnOgI54njyX31Nb6+4P7FXBph374qZN/rCAXuCicgFjbn6GnS533r28hJvOHt0it9MYypF701ZDky2tzRHubWx5c1UfqDax3Fn7dFQpRy6+p+raei64+1Wa1XsU9rFrTuC/nl/F4rXbOG54Xx68YkLCba8744i4jMB1ZxzREUk2abKAXuDCuYBL71sUC+YAuxua+d9XaloE9H1N2mK6orRDklyUWpsjvPDYobxZ93bcvMoellvKlEIu5fiPJ97G71KiWWHaPQvY637v81dv4ZL7FiUM6v51IhtdS5v2s4BeBIK5gJ0Rvb29m6T1elBDko5lABKPUmtS8XOEnzR4QX31hzuSrj9twlCeWbaB+e6RQ4AvHNU5W3vnq0It5VgZOrf2hm7eX313C8kcMagX9bv3Wd8TecjKUItIolboh/bvkdb2zZo8oH/pMwe1NknGGTesksmjBwGgwBNLP4h7FC1K7dbdcdMvrdqcreSZLOroHt1StW9N9jOvrq3nwt++yn8+u5ILf/tqwT0S2dlZQC8iUTnxAT0rGNSnW4suNMtDj8KVlwoloSx49/KS2Eg4Anz9+OEZTG3xCea2AR6trku4bnVtPbXb4gP66k3Jc/Um/3R0nwGpbhIheUC/+6V38ZtvNDZ70yZ/WEAvIv1Cdaw9K0rZvHMfL6/ewo1/eTsuqIeDd0OTtmiINaxfj9jAMYrXW5xpuxJJPh0U9cyyiBRdjqmz91fe0X0GJLtJ9PUOPMIaPr7vbd4Zt2542uSWBfQ8ke0L08xF69gc6oc9XJ9+Z6C3uN4Rj0UFHdC9rMWjU39/58N2prK4jQhVfYSng/xR94IamrQge4ZLpBB6xMtWj26JJLtJ9A3oET+iY/D49g1lCsLTJrcsoOeBbF+Ygv22J7OnYX8O/OihyRsDlZeWtMixb9yxN60iPRPtsIHxjYze2fBxwuPpj7oX9klD5+sZrq03s4XQI15Hj86W6ncN8N5Wr2ouneO74xPrKS6fWEDPA9m+MN30ROpgDjBpZP/Y66tPOTTpul1KvW5nwx5csLY1STMB5x9TRUWg7cLOvU0t+mn3JWsF/3CgG9h859/M/uLZlVz42wWtGg61o3O3rZXujUpwrIZsS6cBbHmpFxaiju/yD+K7f16xcUenLBkpVBbQ88DEEf0oKxEEKC2RjF+YEj1tNrh3l7jpkYEcYqqLS++u5ZHPoO5usN7k2mrcsEoevur4Fg0S/7CwZYB+cukHCd+nbvsnGU9btiys2crehmYUaGxWbvzL22kHiHwaezwsX6sDFqSRWejhqtKiju+eiMder390acbTadrGAnq+8BuhddDD3CXA/zn98Lh54XrZZPVt6fTtblpv3LBKmkN3YJ80xF9Ex9z8DMkfIOw8Jo7o1+Kz3JRG9ZCvI3O3rZGv1QEH9u6acp0+3fZfB8LHt3dozAeAD7bvyVwCTbtYQM8DC2u20tjk5VKamjrmx18z4/Ms++CjuHnh6a5liU+PnXtT151dN+sNxt7yHNfNeqNtiSwy1bX1fOPBJS1KVLoGuuirrq2P7BwoaGzVAdlIXlaMG1YZV80AsHZr5x/kJ1+rA6455VCS/KwBOONTByZc9v2zPtViXr58NmM9xeWF9vQbHRyfuGdFKctumdxifiJbduxNOp2s+PyjPQ0Jl4EXzJ9wxcL+/zsuOjrpNsWsurae83/zauSycldUkmydoAFp5ML898uHrk0PO7An72zY3ybgkDQ7Ospn+dp17LhhlTxy9QkJzyMBenVrmQv3+dVsP3tmBbv2NnHCof2S9v1uOpYF9DzQ1h9/OGjv3NfEmJufoaxU2jRO8fpQRyXJ+HmqilKJ6/fdz209vyL+EbbwtIn3H0kaLvrH92dz0nuC4J1QSUsUv8evxmYoK4FHrj4hZ0Hn1nOOihss5NZzjspJOjKtM3Ydq8ATr9dx7WmHJVwnaujafLk5LHYW0PNEW378UUE7VXEswMgBXg5oQK/4RnErNu5gxuwV9OpWnrKUYJ+rQw/Xfza5bqaG9u0el+sa2rd7ynQVs3VJbqb8serXbEqvE48NH33CjNkrmD51VMJ1onr8+t0l49NPcAaNG1bJY9ecYAGhA/iN9ZJZvXkX5/z6FZ741kmtes9sjh1v0mMBvcD0rChNGdTnXn8qAOcdU8VDoceE7p5fg+DV+yUzpI8XoJtCFb5Nzd4PfNLIAXEB/WsTh6f3AYrUGaMGxqomwr7waa+P/EMP7Mm2talbSzer9z0CCYN6OBefTq4+mzpjbrYz8hvrpbK0Lv3zoSPGjjfpsUZxBWbZLZPjnicPu2bSiNjrRD86hZQ/+iljvIFEupW3HFP1/N+8yj0v18TNS9QRivEka1/g98E/fUriHHeUh5I8j7439JRCeNoUJr+9TibMXLSOr9+3iB17GvKyAWAxsoDeia2d8fnI6QevmMCkkf3pWh7/9V4zaUTSYtiwYUmKyZdv8DqY+PrEYZHLwy21o7oqNfsle075vc07024QF7Q7SUlN+LFDewyxOPjtdQ47sGe73sfvffLl1Vu4e34N/Xp24fRRA624PcesyD2PzFy0jjnLNjBlzODITluihIO6LxMtT68+5dCEXcZOGeONvT196qhY8W4y4UfiTLxkDd4amrTVwRwSdygE0NCoSadNbl1y3yIWr93GccP7ZrQVud947fITD+GHTy2joalt3/ucZRvipt+v38P79Xs49YgDLaDnkAX0PBHsb/1lN4xmukG9PUok8YV/3spNkfMrSqXVaXsjT3rKyldvJamzDA+Tmq5kXRSFi+asqC5/XHLfothQuvNXb+GS+xZlJKhX19bHPU1w2zlHUb97Hzv2NKR1Ux5syT56cO/YdSrojudXdsh1y0Sz33GeCN/xhqez5YBuie/pajbvjDxBVt0+tdX7sT6fkwsPdJMJByepMgnvzQrc2y9TIyYuXrst6XRbXf/o0tjNe7PCT2e/w7WnHcaZowdRFuoWsk+oR7hwV7Y1W6I7/7HBWnLLAnqe8IuwE01ny8gDeyVcdsiAntTM+HzsJCkhuoj/nLEHpbWv3770bhtSWByyUeB9UpLGkT0qSpNOF4qOGi+9uraer/7OG2Tmq79rX9/txw3vm3S6rcJdtO7Y28R1s97gonsW0Bgqpvt4d3zHUQtrtvJJg9eSfW9DM/NXbY7cR88uVuibS3b084RfTNXaOvT2+vDjvZHzy0q8biLB6yY2Gb+F9uy3N1BeWsK4YZWxIsP4fXWeQUM6m0G9urBpx964nPbiUBfCwTYa/Xt2YfPO/U8e9O8Z3ydBNsyYvYJnlm9k8uhBrWqc2VYd+Xz0n1+viz0Zsq+xmT+/XtfmfT14xYSs1KFPHNGvxe/yr29taPHoKXglNofd+DfW/MT77e8I9AypwCcJSpR2pNEltMkeC+h5JKoHpmx7f3vL+tnPHTmQq085tFUXpDsuOjoW2O98cU1kQI8abtXAOb9+pV3bjxzQg385aQQ/f/afbA/krFZv3sV1s97gjouObtFGo6pPfPewmXqUKShY5zp3+cZYPW2qZ+RTvVe652VHPh8dDontLXHJRneq3znj8Ba/S03ScjIYs/2nWlLJVGmCaRsrcu8kslV0WFbS8hR47p0P23XhmziiX4sGWSMH9LDGMgks+yC9i2UUP5jf+Je344K5b/bbXluMR16L70BoY6i05PgMPzscrnN9Yun7ccufWb6xze+V7m+gIwdIOf+YqtigJ2Ul3nS+uTuiyitZtUxQOlWAIwf0sH7dc8wCeieQzbGVP52FkbnGDavk8W+ewLC+3akoFSaN7B/rnc60NKh324q7f3LuUcy9/lTufyVxC+USNxxvONiHS0yTDcjRFuHccbiB3uTRg9r8XumORtjR46WXlJQg7n8+em9zfNfBB3Qr48ErJvCTc4+KHFmxPDAK3rQJQ/nJuUdx8sj+/OTcoyKHVn53c+cfJa+zy88zz8TJ5tjKN7Sy97F0jRtWyUvfP41Vt0+1u/YU+oXqr5M9eRBl+yeJR77zb9iS9RUP8XWkmRDOHZ97dBV+fCgVOLMVAb09Oe2OGi89F0Mgt9aIAfGdyUw4xDuO0yYM5Z+3TSE0ii39esR3BjVtwlD+cMUEpk0YykmHtczZ25MSuWcBvRPIZtGhdQKRe+Fudg86oFvSZ8h99//jPQD6dEvcC98NU0Zx3aw3UtbpPhwqkm+vcO64fve+uDS0JuB1dE67LfJ1/POgEaFhacPTgw6Ib1dRGpUNd+wmPT9Zo7hOIF/HVjaZsb4+Pve8ZedevjT2oISDtfi27/FaqV9+4iEJe/T7w4K1zEvwiFHQR20YbjeV8IArFWUlNDQ2tyng5fvgLfnwG03VcDDcsC08HX4KxZ5K6XwKLqCLyP3A2cAmVR3j5vUFHgGGA2uBr6hqvVv2A+AKoAn4tqo+6+aPAx4AugGzge+oas76x0z3gjZ8+t9irxN1Cxt2Tih4pPtcuWm/mYvWsXNvfJ/rH+1p4I6LjubJpR8kzVn36erVewcfeVzw7ta4Z4qfXb6Rs0YPSnlzAHDmL+dlra1DPgS8bMvlTUc6j+hNGTM4rne3cEO3cLuK4PSM2St4Yun7DO3bnRumjGLcsEq6lZWwJ7BStyw8KWFapxC/gQeAyaF504EXVHUk8IKbRkSOBC4CRrtt7hIRv4eN3wBXASPdX/g9804wmEdNJ3LHRUdzztiD6NO9nHPGHpR05C+TWVE9AvrPBZeFKzVDzhg1MPbar9/s1TW+g5iu5SXccdHRHNgr9eA4q7PcqKmj6rMLjT+q2cxFiatF0mlnE27YFn7qJNHZNmP2Cu6eX8PGj/eyeG09X/ntAqpr61lx25RYEO9WVsKK26a0+TOazCi4HLqqzheR4aHZXwJOda9/D8wDbnDzZ6nqXuA9EVkDHCcia4HeqroAQEQeBM4B5mQ5+TljQTw3wrkmgMqeXvBtTDFwxmPV61s8y33s8H48986HcdMA5x1dlbK/7hT3D+2WqEi4LYMSFYt0x3jw6/BTVWkk6+uiV9cyPg513VpdWx9rq+FratbYM/0WxPNLIebQowxU1Q0A7v+Bbv4QYH1gvTo3b4h7HZ7fgohcJSJLRGTJ5s2p6yqzZczNz+Rs36btpk0Y2qKTl6oDuqW17dZdLVumX33KobHHjcpLhatdb3/vJuh7O6hJkw/j2h6JHr0MDsN541/eTpoLLUbpjvGQiYaD045rGeh/NmcF+yJuLPOx0Z8pnoCeSFSeRJPMbzlT9R5VHa+q4wcMGJDRxLXGzoixr0cO6BGxpsk372+Pb3z0pht5rTxFlrl7aLz7mYvWccfzq7jixEP43llHMOuq42MX9k1pNnDK1uNWiYqE73pxddx6P39mRcri5WLSmjEe2lulEfUoYaLHHa3aJD8VXJF7Ah+KyGBV3SAigwF/XNA64ODAelXAB25+VcT8vNWzorRFULfOXDqHRN2Gdq8oZV+S1uc3nT069jpcNPuTc4+Ku+heeOxQ3qyLbgkflK2cV6Ii4U074scS2L6nkZdXb+nQIYTzWUeO8RDVk9w5Y4ekNbSqyQ/FkkN/CrjUvb4UeDIw/yIR6SIih+A1flvsiuV3iMhEERHgksA2OZGq69evTRwWN33NpBEdkSyTRf17dY2cf9iBPVs0agr3FheenjZhKMcNT56rKpHs5LxmLlrHLX9dzqeHHMBFxw2NKxKOKs71BT/DsbfNZfj0v3HsbXMznr58F+zQpb2SXUfeWNdy3h8X1rZ7n6bjFFxAF5GHgQXAESJSJyJXADOAM0VkNXCmm0ZVlwOPAu8AzwDXqqqfzf0mcC+wBniXHDaIS6fr1z+EfnjhaZO/JoX60/anLz/xkMj1e1SUtri4fxTq6S08DanHqm7OQh26X3LwZt1HLF5bz6NL1qfeyNm8y8u9H3vb3NjIcJt37ivKoJ4Jqa4jUeO0RFXlZbvxpGm7ggvoqvpVVR2squWqWqWq96nqVlU9XVVHuv/bAuvfrqqHquoRqjonMH+Jqo5xy76Vy2fQ03kkpaGpOem0yV8PXjGBSSP707W8hEkj+6fshevA3i1z7sGhUKOmAerqk3f/Cl4jqEwK15E3NGnc+VuRJDp8ss87h9P5bCa1VNeRr4xLb0CZd3+aXv8WpuMVSx16pxR8jry8VJBmTfhIytSjBsd1HjL1qNSjI5n8ERXEo1o0B8epb60DupWzY2/LHFfQ6xHFru2xOyKHFzx/V90+lcP/fXbSovcBPSvigviAnqmfqTctpXq0bfrUUdz7Sk2LDmZM51FwOfRCMHPRuhadwjQ0KU0KnzQ0c+tfl7fYxjqHKTzhFs2fO3Igj1x9QmQ9d7iXrqheu/71tJEp95npgp2hoVHWqvp0bZH+VbdPZe2Mz7cI1L27evmN1246M7ZsQM8KXrvpzMwmskik82hbZXe7WerMLIeeZ4KtlRNZWvcR5/z6FZ741klx8y2IF5bWtHBecdsURt00hz2Nze3qteszGR5Od9Wm+CE767Z/QnVtfWQwOaR/j7ic+CGBwUMsiGdGqu5pxw6tZG6gYyLTuVhAzzOPpDnq1bIPPk69kun0kvXsFeYHcb8lc7hHtkSdkgSFbxLbK6oth9/LWNjIgb1YvLY+btpkVqoBXK455VBe/OeHNDZ7jd/CNSHWt0V+s4CeZyrSHOBgzEG9s5wSkw9a2y1qskE6ws3PJo3sz/xAt7PpDubTGocN6MmKjTti00LiZ93PO6aKx6rrYnW85x2TXiMtk550BnAZN6ySR64+gYU1W3lz/fa4boR7dSm1vi3ynAX0PJNOHVafbmUZz0mZ/BPuLOb+f7zH5ScekjSwL6zZyt6GZhTY19Aclxte8G58n/EL3t2SlSAedNu5R3HB3a/SrF4wvz3U4U3QuGGVPPyNwh6RLZeiWrlHHWO/WP6qB5fEzT/+0P4t1jX5xQJ6nhnQq0uLed3KS+nXo5yd+5o49fABVldeJH41d2Xc9JpNO2MBPlFQr+xeEetprpn4G8TGUPFpeDobxg2r5LFrTuDul95Nq/vZfB/3PFtmLlrHI6+tY2Dvrlx9yqFZOQbpDuDi2757X9Jpk38soOeBYL2WX+y4r7EZEbj65BEtRtQyxeHjBN2+zlm2IWFAr9+9jxLxOgkpEW/aN+SArtQF+o0fckB0T3SZtnLjjlhDK7/7WT/9qep0i0F8Q9iPeHHlprh++DOltWPS7w09vxaeNvnHAnqORdVrWbGjAejbo5yNH+9tMT/ZAB3JcmH9enaJC+j9erYsDcqGcEPPR15bx7QJQ9Oq0y0G4caKfuc72TgWrSkBCff/f+Gxxd2vfmdgAT3Houq1rj3tML7z8Ov84tmVDOnTlVemn57rZJocGNi7a4uAXlEqSevQk+XCBvbuCnwUms6+LqGGnv50unW6hW7KmMGxwWjA60QqH4Yn7ciBYUxmWEDPsagc1UkzXojlpOq2f8JJM16woF6EokZIS9ajmi9RLuzqUw7lxZWbaGjSuLHSsy3R42itrdMtVH6gzHYdemud+ct5rN68C4B/bvjYAnonIDnsorzgjB8/XpcsWZJ6xZBwPeIh0/8WN6SmAO9luTWyyU/hHgOhfY+XVdfWxxqoXXhs+s+4t0d1bT1fvvtVVEEEHr9mf293Voeen4LB3JfNXvpEpFpVx2flzYuI5dDzQDhHNaRPqPFSn44pGjWFyw+ci2q2xp49DzdQy5b/en4Vfr5B1Zv2+64v1lbt+e7dLbtazLNBcfKf9eWeh16ZfjpVfboieH1fW3F78Qr/QNvyg/Ubn/3nsyvjOpKB9HqPa6/Fa7clnQ4r5rHP88Wh/Vv2CGeD4uQ/C+h56pXpp/PejM9bMC9yt517VNLpdPiNz6IkazGfKccN75t0OsjGPs8Pc68/Na6bVxsUp3OwIndj8lgmWhr7jc8+aWgZ1I8YlP3+0h+8YgKX3LeIxWu3cdzwvknHe7exz/OHdfPa+VijuAxqa6M4Y7LNbwwXHknruOGVPHrNCTlKVUvBHDpYzrBYWKO4zLAid2OKwLhhlZHdCi+prY9YO3ds7HNj2s6K3I0pQDMXreP+V2pAJDagS3i0NfC6iO1I6TymZkHcmLaxgG5MgYnvG5zY6/OOqeKhResSbZZ11tWrMdllRe7GFJioR9HmLNvAuGGVVIX6NAi2ZM62qK5ejTGZYwHdZFR1bT13vriG6jyrmy0mUY+i+fNemX46Iwf0oES8YN6RLZn91valQlF39WpMtlgr9wwq9lbuVqSaP0bdNIc9jc0IcPWk/BmC17p6NVGslXtmWA7dZIwVqeaHsT9+lj2uIxkF7p5fkzclJuOGVXLtaYdZMDcmCyygm4yxItX8sH1PY4t5dnNlTOGzVu4mY5KNxW06ExC1+AAACKZJREFUTp9uZS2Cut1cGVP4LIduMsqKVHNv6Q/Pok837169a1kJf/rmCfZ9GFMELIduTAFa+sOzcp0EY0wHsxy6McYYUwAsoJuMs2fRzcxF6/j6fYuYmcOe6YrRjNkrOPU/X2TG7BW5TorJAStyNxllz6KbYNezL6/eAtCmYV9N68yYvYK759cAxP7nS/8DpmNYDt1klD2Lbv490I981LTJjmeWb0w6bQqfBXSTUfYsugn3PWl9UXaMyaMHJZ02hc+K3E1G2bPopqJU2NekcdMm+/zi9WeWb2Ty6EFW3F6ErC/3DCr2vtyN8R3+77PZ16RUlAqrbp+a6+SYPGd9uWeG5dCNMRlnQdyYjmd16EmIyGQRWSkia0Rkeq7TY4wxxiRiAT0BESkF7gSmAEcCXxWRI3ObKmOMMSaaBfTEjgPWqGqNqu4DZgFfynGajDHGmEgW0BMbAqwPTNe5eXFE5CoRWSIiSzZv3txhiTPGGGOCLKAnFvWsTYtHAlT1HlUdr6rjBwwY0AHJMsYYY1qygJ5YHXBwYLoK+CBHaTHGGGOSsoCe2GvASBE5REQqgIuAp3KcJmOMMSaSPYeegKo2isi3gGeBUuB+VV2e42QZY4wxkSygJ6Gqs4HZuU6HMcYYk4oVuRtjjDEFwAK6McYYUwAsoBtjjDEFwAK6McYkUV1bz50vrqG6tj7XSTEmKWsUZ4wxCVTX1nPxvQvZ19hMRVkJD105kXHDKnOdLGMiWQ7dGGMSWFizlX2NzTQrNDQ2s7Bma66TZExCFtCNMSaBiSP6UVFWQqlAeVkJE0f0y3WSjEnIityNMSaBccMqeejKiSys2crEEf2suN3kNQvoxhiTxLhhlRbITadgRe7GGGNMAbCAbowxxhQAC+jGGGNMAbCAbowxxhQAC+jGGGNMAbCAbowxxhQAUdVcp6FgiMhmoBboD2zJcXLyhR2L/exY7GfHYj87FjBMVQfkOhGdnQX0LBCRJao6PtfpyAd2LPazY7GfHYv97FiYTLEid2OMMaYAWEA3xhhjCoAF9Oy4J9cJyCN2LPazY7GfHYv97FiYjLA6dGOMMaYAWA7dGGOMKQAW0I0xxpgCYAG9HUSkq4gsFpE3RWS5iPzYze8rInNFZLX7XzRjL4pIqYi8ISJPu+miPBYislZE3haRpSKyxM0r1mPRR0QeF5F/isgKETm+GI+FiBzhzgf/72MRua4Yj4XJDgvo7bMX+KyqfgYYC0wWkYnAdOAFVR0JvOCmi8V3gBWB6WI+Fqep6tjAM8bFeiz+C3hGVT8FfAbv/Ci6Y6GqK935MBYYB+wG/kIRHguTHRbQ20E9O91kuftT4EvA79383wPn5CB5HU5EqoDPA/cGZhflsUig6I6FiPQGJgH3AajqPlXdThEei5DTgXdVtRY7FiZDLKC3kytiXgpsAuaq6iJgoKpuAHD/D8xlGjvQHcD3gebAvGI9Fgo8JyLVInKVm1eMx2IEsBn4X1cVc6+I9KA4j0XQRcDD7nWxHwuTIRbQ20lVm1wRWhVwnIiMyXWackFEzgY2qWp1rtOSJ05U1WOAKcC1IjIp1wnKkTLgGOA3qno0sIsiL1IWkQrgi8BjuU6LKSwW0DPEFSPOAyYDH4rIYAD3f1MOk9ZRTgS+KCJrgVnAZ0XkjxTnsUBVP3D/N+HVkx5HcR6LOqDOlVwBPI4X4IvxWPimAK+r6oduupiPhckgC+jtICIDRKSPe90NOAP4J/AUcKlb7VLgydyksOOo6g9UtUpVh+MVJ/5dVb9GER4LEekhIr3818DngGUU4bFQ1Y3AehE5ws06HXiHIjwWAV9lf3E7FPexMBlkPcW1g4h8Gq8RSynezdGjqnqLiPQDHgWGAuuAC1R1W+5S2rFE5FTg31T17GI8FiIyAi9XDl6R80xVvb0YjwWAiIzFayhZAdQA/4L7vVB8x6I7sB4YoaofuXlFeV6YzLOAbowxxhQAK3I3xhhjCoAFdGOMMaYAWEA3xhhjCoAFdGOMMaYAWEA3xphOQERuFZG33MAuz4nIQQnWazEwkJv/IxF5PzA4zFQ3/+LQoDHN7skEROSZwOBTd4tIqZt/mYhsDmxzZYq0Rw5Mk7mjY8BauRtjTN5xj35epqqXBeb1VtWP3etvA0eq6jUR264FxqvqltD8HwE7VfUXSfZ7FPCkqo4I7lNEBK9ToMdUdZaIXOb28a02fLZS4H1gguvL3mSI5dCNMaYT8IO50wNvvIBMi+v0JrDPMrx+BFLuU0S+JyKvudKEH0esEhyYxmSQBXRjjOkkROR2EVkPXAzcnGC1qIGBfN9ygfb+BOOuX0h8L3aIyLN43dHuwMul+8537/W4iBzs1v0cMBKvq+OxwLiIcQyCA9OYDLIid2OMyRMisgjoAvQE+uL1HAdwg6o+G1jvB0BXVf1hxHscpKofiMiBwFzg/6jqfBEZCGzBC/i3AoNV9fLAdhOAe1X1qIj37Ao8BNytqnNd73Y7VXWviFwDfEVVPysivwC+DGx3m/YEfqqq97n3qQA+AEYH+rI3GWIB3Rhj8kxUHXpo+TDgb6qadHTHRPXmIjIceDq4vYj8P2Czqv4kwXtdChwbrjd3deLbVPUAEfklsEpVf5vgPb4EXKuqn0uWbtM2VuRujDGdgIiMDEx+EW8gqPA6iQYG8kdy853rz3fLSoAL8EZK9Of1DIwCVwZM9fcZeq8vAivc62eBy0Wkp1tviCsp8IUHpjEZVJbrBBhjjEnLDDdqXTNQC1wDXhE7XlH5VGAg8BevUXpsYKBn3PY/d4+jKbAWuDrw3pPwhrmtCczrATwlIl3wBqD6O3C3W/ZtEfki0AhsAy4DUNXnRGQUsMClYSfwNWCTG5jmzNB+TQZZkbsxxhhTAKzI3RhjjCkAFtCNMcaYAmAB3RhjjCkAFtCNMcaYAmAB3RhjjCkAFtCNMcaYAmAB3RhjjCkA/x8N1qroL6zn4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib widget\n",
    "for file in [files[0]]:\n",
    "    this_D6=ATL06_to_dict(file, dataset_dict)\n",
    "    plt.figure()\n",
    "    plt.plot(this_D6[1]['delta_time'], this_D6[1]['h_li'],'.')\n",
    "    plt.title(this_D6[1]['filename'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! ls -al ./outputs/baker/**/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./outputs/baker/161027313/processed_ATL06_20181019231110_03260102_001_01.h5'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "\n",
    "\n",
    "ds = xr.open_dataset(files[0])\n",
    "# ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                (delta_time: 689)\n",
       "Coordinates:\n",
       "  * delta_time             (delta_time) datetime64[ns] 2018-10-19T23:16:51.413560248 ... 2018-10-19T23:16:53.750269712\n",
       "    latitude               (delta_time) float64 ...\n",
       "    longitude              (delta_time) float64 ...\n",
       "Data variables:\n",
       "    atl06_quality_summary  (delta_time) int8 ...\n",
       "    h_li                   (delta_time) float32 ...\n",
       "    h_li_sigma             (delta_time) float32 ...\n",
       "    segment_id             (delta_time) float64 ...\n",
       "    sigma_geo_h            (delta_time) float32 ...\n",
       "Attributes:\n",
       "    Description:  The land_ice_height group contains the primary set of deriv...\n",
       "    data_rate:    Data within this group are sparse.  Data values are provide..."
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset(files[0],group='/gt3r/land_ice_segments')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_mfdataset(files,group='/gt3r/land_ice_segments')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                (delta_time: 726)\n",
       "Coordinates:\n",
       "  * delta_time             (delta_time) datetime64[ns] 2018-10-19T23:16:51.413560248 ... 2019-01-18T18:56:39.930891940\n",
       "    latitude               (delta_time) float64 dask.array<shape=(726,), chunksize=(689,)>\n",
       "    longitude              (delta_time) float64 dask.array<shape=(726,), chunksize=(689,)>\n",
       "Data variables:\n",
       "    atl06_quality_summary  (delta_time) int8 dask.array<shape=(726,), chunksize=(689,)>\n",
       "    h_li                   (delta_time) float32 dask.array<shape=(726,), chunksize=(689,)>\n",
       "    h_li_sigma             (delta_time) float32 dask.array<shape=(726,), chunksize=(689,)>\n",
       "    segment_id             (delta_time) float64 dask.array<shape=(726,), chunksize=(689,)>\n",
       "    sigma_geo_h            (delta_time) float32 dask.array<shape=(726,), chunksize=(689,)>\n",
       "Attributes:\n",
       "    Description:  The land_ice_height group contains the primary set of deriv...\n",
       "    data_rate:    Data within this group are sparse.  Data values are provide..."
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gdf = gpd.GeoDataFrame().reindex_like(df)\n",
    "for file in files:\n",
    "    df = ATL06_2_gdf(file, dataset_dict)\n",
    "    gdf.append(df)"
   ]
  }
 ],
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